Christos Iossifidis - UZH · Master Thesis in Banking and Finance . Christos Iossifidis . Advisor: Annette Krauss . Full Text Version . Country risk management of microfinance investment

Department of Banking and Finance Center for Microfinance

CMF Thesis Series no. 9 (2010)

Country risk management of microfinance investment vehicles Master Thesis in Banking and Finance

Christos Iossifidis

Advisor: Annette Krauss

Full Text Version

Country risk management of microfinance investment vehicles Master Thesis in Banking and Finance Author: Christos Iossifidis Advisor: Dr. Annette Krauss Professor: Professor Dr. Urs Birchler Full Text Version Center for Microfinance Thesis Series no. 9 (2010) Zürich: University of Zurich, Department for Banking and Finance / Center for Microfinance Plattenstrasse 14, 8032 Zurich, Switzerland

I

Executive summary

Microfinance investment vehicles (MIVs) emerged recently to grow at an impressive pace.

MIVs receive their revenues from emerging markets and developing economies, whose de-

velopment standards are not comparable with industrialized countries. A recurring concern

amongst microfinance practitioners is that political interference occurs in an “interest rate

ceilings” form. This study aims at investigating the impact of country-specific factors on MFI

portfolio quality underlying particular MIVs. Concentration risk of MIVs is researched.

Moreover, the effect of an interest rate ceiling policy adversely impact portfolio quality is

addressed. The scope of study is solely limited on country-specific aspects. While the pur-

pose of the research is not to assess a domestic “banking crisis”, the study will enlighten

country risk factors impacting microfinance from an investor’s viewpoint, specifically consi-

dering a MIV portfolio investing in Latin America.

II

Table of content

1. Introduction: Why does country risk matter for microfinance? ............................................ 1

2. Microfinance: perspectives for a socially-motivated investor ............................................... 3

2.1 Notions of microfinance ............................................................................................................ 3

2.1.1 Microfinance: a concept to alleviate poverty................................................................... 3

2.1.2 Microcredit: a tool to alleviate poverty ............................................................................ 6

2.2 Microfinance institutions: the micro level .............................................................................. 7

2.2.1 Definition and overview .................................................................................................... 7

2.2.2 Microfinance institutions in the landscape of financial service providers.................. 9

2.2.3 Specific features and lending methodologies of microfinance institutions .............. 10

2.3 Microfinance funding environment ...................................................................................... 12

2.3.1 Sources of funding for microfinance institutions: an overview ................................. 12

2.3.2 Degree of commercialization and issues ....................................................................... 14

3. International funding of microfinance .................................................................................... 17

3.1 Primary cross-border funders ................................................................................................ 17

3.1.1 Classification of primary funders ................................................................................... 17

3.1.2 Global overview and actual issues of international funding ...................................... 18

3.2 Microfinance investment intermediaries .............................................................................. 22

3.2.1 Definition of microfinance investment intermediaries ................................................ 22

3.2.2 Types of microfinance investment intermediaries ....................................................... 23

4. Microfinance investment vehicles: a dual investment opportunity ................................... 24

4.1 Classifications: in constant evolution .................................................................................... 24

4.2 Actual trends and implications for microfinance ................................................................ 27

5. Country risk: implications for microfinance ......................................................................... 33

5.1 Country risk as a broad concept ............................................................................................ 33

5.2 Geographical allocation and concentration risk .................................................................. 33

5.2.1 Global overview ................................................................................................................ 33

5.2.2 Country exposure in Latin America and the Caribbean ............................................. 35

5.3 Global Partnerships Microfinance Funds ............................................................................. 40

III

5.3.1 Overview of Global Partnerships’ MIVs ........................................................................ 41

5.3.2 Country exposure and concentration ............................................................................. 42

5.3.3 Capital structure and MFI debt default ......................................................................... 44

5.3 Interest rate ceilings ................................................................................................................. 47

5.3.1 Why are interest rates so high in microfinance? ........................................................... 47

5.4.2 Interest rate ceilings can damage microfinance and affect the poor .......................... 48

6. Country risk and microfinance: a regression approach ....................................................... 51

6.1 The research model and preliminary analysis ..................................................................... 51

6.1.1 Impact of country risk on portfolio quality in microfinance: a literature survey .... 51

6.1.2 Research questions and econometrical model .............................................................. 52

6.2 Selection of variables ............................................................................................................... 53

6.2.1 Portfolio quality ................................................................................................................. 53

6.2.2 Country risk factors .......................................................................................................... 55

6.2.3 Institutional factors ........................................................................................................... 59

6.3 Empirical results ....................................................................................................................... 61

6.3.1 Data and descriptive statistics ......................................................................................... 61

6.3.2 Regression results ............................................................................................................. 62

6.3.3 Conclusion.......................................................................................................................... 64

7. References ................................................................................................................................... 65

Appendix I: Key principles of microfinance .................................................................................. 73

Appendix II: Advantages and disadvantages of debt versus equity .......................................... 74

Appendix III: Top 10 MIV in 2008 ................................................................................................... 75

Appendix IV: MIV survey participants .......................................................................................... 75

Appendix V: Descriptive statistics ................................................................................................... 76

Appendix VI: Correlations ................................................................................................................ 77

Appendix VII: PAR-30 ....................................................................................................................... 79

Appendix VIII: PAR-30 without time effects and country dummies ......................................... 80

Appendix IX: PAR-90 ........................................................................................................................ 81

Appendix X: PAR-90 without time effects and country dummies ............................................. 82

Appendix XI: WOR ............................................................................................................................ 83

IV

Appendix XII: WOR without time effects and country dummies .............................................. 84

Appendix XIII: (PAR-30+WOR) ....................................................................................................... 85

Appendix XIV: (PAR-30+WOR) without time effects and country dummies ........................... 86

Appendix XV: LLR ............................................................................................................................. 87

Appendix XVI: LLR without time effects and country dummies ............................................... 88

Appendix XVII: RCR ......................................................................................................................... 89

Appendix XVIII: RCR without time effects and country dummies ............................................ 90

V

List of figures

Figure 2.1: Defining Microfinance Clients

Figure 2.2: Regional distribution of microfinance clients

Figure 2.3: Regional distribution of MFIs

Figure 2.4: The spectrum of financial services providers

Figure 2.5: Process of group-lending-based contracts

Figure 2.5: Types of MFIs according to their degree of commercialization

Figure 3.1: Committed amount by type of funder and by region

Figure 3.2: Committed amount (US$ million) by region

Figure 3.3: Microfinance investment growth by investor type

Figure 3.4: International investor landscape at end 2008

Figure 4.1: MIV asset growth 2005-2008

Figure 5.1: Geographic concentration in 2008 (MicroRate)

Figure 5.2: Geographic concentration in 2008 (CGAP)

Figure 5.3: Market penetration and interest rate ceilings

Figure 6.1: Cross-Country Comparisons of PV and standard errors

List of tables

Table 2.1: Average loan balance per borrower by region

Table 2.2: Shares of total funding by institutional type (2005-2007)

Table 3.1: Landscape of primary cross-border funders

Table 4.1: MIV peer group classification by legal structure

Table 5.1: Outstanding MIV assets in 2008

Table 5.2: MIV country exposure analysis

Table 5.3: Outstanding GP’s microfinance portfolio

Table 5.4: Capital structure

Table 5.5: Investor’s Banana Skins

VI

List of abbreviations

AUM Assets Under Management

CDO Collateralized Debt Obligation

CGAP Consultative Group to Assist the Poor

CLO Collateralized Loan Obligation

EAP East Asia and the Pacific Region

ECA Europe and Central Asia Region

EIB European Investment Bank

EU European Union

FINCA Foundation for International Community Assistance

GDP Gross Domestic Product

GNI Gross National Income

GP Global Partnerships

HHI Herfindahl-Hirschman Index

IDB Inter-American Development Bank

IFI International Financial Institution

IPO Initial Public Offering

KfW KfW Entwicklungsbank (former name: Kreditanstalt für Wiederaufbau)

MEF Microfinance Enhancement Facility

LAC Latin America and the Caribbean Region

MII Microfinance Investment Intermediary

MIV Microfinance Investment Vehicle

MIX Microfinance Information eXchange

MFI Microfinance institution

MFIF Microfinance Investment Fund

MENA Middle East and North Africa Region

NBFI Non-Bank Financial Institution

NGO Non-Governmental Organization

OLS Ordinary Least Squares

PaR30 Portfolio at Risk greater than 30 days

SA South Asia Region

SRI Social Responsible Investment

SSA Sub-Saharan Africa Region

1

1. Introduction: Why does country risk matter for microfinance?

In recent years, countries in Latin America (e.g. Bolivia) and worldwide (e.g. Indonesia)

have been subject to domestic microfinance crises that have seriously affected local micro-

finance institutions (MFIs); fortunately, these crises remained to some extent within country

borders, thus preventing a spread in the regional and global microfinance sector. Past crises

occurred when microfinance was considered more as a “movement” than an “investment

opportunity”; when foreign capital in microfinance originated predominantly and almost

exclusively from donors and development financial institutions.

Microfinance investment vehicles (MIVs) emerged only recently to grow at an impressive

pace in terms of capitalization, and to draw the attention of the microfinance community. A

main issue to consider is that MIVs receive their revenues from emerging markets and de-

veloping economies, whose development standards are not comparable with industrialized

countries. Therefore, foreign investments in developing countries are “subject to a varying

degree of restrictions and controls” (EMF, 2008, p.16). Currently, cross-border investments in

microfinance from MIVs are largely concentrated (in terms of volume) among 200 MIVs in a

few nations, making country risk a critical factor of MIVs’ soundness and financial perfor-

mance.

Furthermore, the broadening of the microfinance industry is leading to its greater integra-

tion within the mainstream financial sector of developing and transitional countries. Accord-

ing to Reuters (2009), this growing integration brings a “political risk” into microfinance;

populist policies adversely impacting the sector. A recurring concern amongst microfinance

practitioners is that political interference occurs in an “interest rate ceilings” form.

"That would make even the most nonprofit organization in most countries very hard to operate if

[interest rate] ceilings were imposed without some reality of what the costs are for those institutions

to deliver services in many more areas." Bob Annibale, global director of Citi Microfinance in

Reuters (2009).

At present, about 40 developing and transitional countries enforce the “interest rate ceilings”

policy (CGAP, 2004a). Nicaragua is amongst them. To date, the Nicaraguan government of

President Daniel Ortega labels microfinance as “usury” and continues to place ceilings on

interest rates of loans contracted by Nicaraguan micro-borrowers (MicroRate, 2009a, p.27).

Additionally, an organized protest movement called “Movimento de no pago” (“No Payment

Movement”) emerged in 2008, poised to prevent the settlement (or payback) of outstanding

MFI loans. The movement was initially disapproved, although later Ortega’s administration

encompassed it and promoted it “as an example of the government's efforts to defend economic

populism.” Pachico (2009, p.1).

2

As a result of the movement, the largest microfinance network in Nicaragua (ASOMIF)

agreed to refinance micro-borrowers debt at lower interest rates (MicroRate, 2009a). Current-

ly, no MFI bankruptcy has been associated to the “No Pago” movement; however, several

MIV managers (e.g. responsAbility, BlueOrchard) reported in October and November 2009

negative performance due to additional provisions made against MIV loans to Nicaraguan

MFIs.

This study aims to provide a comprehensive country risk framework in regards to microfin-

ance.

3

2. Microfinance: perspectives for a socially-motivated investor

This chapter is designed to highlight essential notions of microfinance and provide an overview on

microfinance institutions.

2.1 Notions of microfinance

“The poor stay poor, not because they are lazy but because they have no access to capital.”

Milton Friedman, 1976 Nobel Prize in Economic Sciences

One of the critical aspects that impacts adversely growth in the developing world is that a

major part of its population is excluded from financial services (Yusuf, 2009). The vulnerabil-

ity these people face could be reduced when means that assist to smooth consumption and

overcome crises are provided. Like all individuals they need a full range of financial servic-

es, rarely accessible through the mainstream financial sector. 1

2.1.1 Microfinance: a concept to alleviate poverty

Microfinance means financial services for low-income people, mainly to start up and grow

businesses. There are many definitions of microfinance, and the different concepts reveal

show a discrepancy. According to the Consultative Group to Assist the Poor (CGAP)2, “mi-

crofinance offers poor people access to basic financial services such as loans, savings, money transfer

services and microinsurance. People living in poverty, like everyone else, need a diverse range of fi-

nancial services to run their businesses, build assets, smooth consumption, and manage risks.”3 In

order to frame the scope of microfinance, CGAP (2004) developed a list of key principles for

“effective, accessible and equitable microfinance services”. The Key Principles for Microfinance (cf.

Appendix I) were endorsed by G8 leaders in 2004.

Based on Stuart Rutherford research, CGAP (2006, p.22) distinguishes three functions de-

scribing the expediency of microfinance. First, microfinance provides low-income people

with the ability to deal with life-cycle events, e.g., marriage, death and education. Second,

microfinance reduces vulnerability by increasing the aptitude to deal with emergencies, e.g.,

personal crises and natural disasters. Third, microfinance provides opportunities to invest in

“an existing or new business, or to buy land or other productive assets” (Rutherford, 2000, p.8).

1 Refer for instance to Honohan (2004) and Bell & al. (2002).

2 CGAP is an independent policy and research center dedicated to advancing financial access for the

world's poor. Housed at the World Bank, Washington, D.C., it is supported by over 33 development

agencies and private foundations who share a common mission to alleviate poverty.

3 Website of CGAP, section: “Frequently Asked Questions - What Is Microfinance?”,

http://www.cgap.org (Accessed on March 15, 2010).

4

Microfinance in Latin America

aforementioned function. Berger & al. (2006

“financial services primarily for microenterprises: their owner/operators and their workers

croenterprise has a broad definition; it includes independent economic activities ranging from ind

vidual vendors selling oranges on the

between.”

The general objective, as stated by all representatives involved in microfinance, is to focus

on those excluded from the formal financial sector. The very essence of microfinance is to

give the individual the tools to develop him

Give a man a fish and you feed him for a day. Teach him how to fish and you feed him for a lifetime.

Targeted microfinance clients are identified by certain cha

caste, religion, geographic location (

1998). In referring to microfinance

them in four groups. Most current mic

poverty line.

� Vulnerable non-poor clients are in

ible to slip into poverty.

� Moderate poor clients are in the top 50 percentile of households below the

line.

� Extreme poor clients are in households in the bottom 10 to 50 percentile of hous

holds below the poverty line.

� Destitute clients are in households in the bottom 10 percent of households below the

poverty line.

Figure 2.

Source: CGAP (2006)

Latin America is slightly narrower, though strongly linked to the third

Berger & al. (2006, p.4) define microfinance in Latin America

financial services primarily for microenterprises: their owner/operators and their workers

has a broad definition; it includes independent economic activities ranging from ind

vidual vendors selling oranges on the street to small workshops with employees



e the individual the tools to develop him- or herself.


Lao-Tzu, Philosopher of ancient China

Targeted microfinance clients are identified by certain characteristics: gender, ethnicity,

caste, religion, geographic location (e.g., rural or urban) and poverty level (Ledgerwood,

microfinance clients by poverty level, Cohen & Sebstad

Most current microfinance clients seem to fall around or just below the

clients are in households above the poverty line but are suscep

ible to slip into poverty.

clients are in the top 50 percentile of households below the

clients are in households in the bottom 10 to 50 percentile of hous

holds below the poverty line.

clients are in households in the bottom 10 percent of households below the

Figure 2.1: Defining Microfinance Clients

Source: CGAP (2006) based on Cohen & Sebstad researches

linked to the third

n Latin America as

financial services primarily for microenterprises: their owner/operators and their workers. […] Mi-

has a broad definition; it includes independent economic activities ranging from indi-

street to small workshops with employees—and anything in




hilosopher of ancient China

racteristics: gender, ethnicity,

rural or urban) and poverty level (Ledgerwood,

& Sebstad (2000) separate

rofinance clients seem to fall around or just below the

above the poverty line but are suscept-

clients are in the top 50 percentile of households below the poverty

clients are in households in the bottom 10 to 50 percentile of house-

clients are in households in the bottom 10 percent of households below the

5

Cohen & Sebstad (2000) find out that microfinance clients come from extreme poor, mod-

erate poor, and vulnerable non-poor households. People coming from destitute households

seem not accessible by microfinance. Amongst few exceptions, the largest number of micro-

finance clients appears to fall in the moderate poor category (Figure 2.1).

Latin American microfinance is no exception; it focuses on entrepreneurs with insufficient

access to financial services and the unbanked and under-banked in general, including clients

both below and above the poverty line. Latin American microfinance aims to provide servic-

es to a broad range of clients (Berger & al. 2006), rather than focusing on the poverty groups.

Therefore, microfinance does not coincide with charity. Microfinance might be a sustainable

approach to alleviate poverty as opposed to a one-off donation. Reformulating Friedman’s

quote, people in developing and transition economies do not lack entrepreneurship, they

lack access to capital; thus economic growth might be limited without capital formation

(Honohan, 2004). At first sight, this might contradict with traditional development work;

however the whole idea is based on helping low-income people to gain independency from

financial aid.

Another traditional objective of microfinance is to assist female populations (e.g., Grameen

Bank). Women are often discriminated in developing countries, and as a result they have

limited access to capital. Commercial banks from the formal sector tend to favor men; con-

sequently women seek solutions through the informal sector (Armendáriz & Morduch, 2005,

chap. 7). The issues surrounding microfinance and gender equity are often sources of dis-

cord among academics and practitioners.

On the one hand, empirical results seem to confirm that microfinance contributes to increas-

ing women’s empowerment (e.g., Pitt & al. 2003). On the other hand, Nowak (2005) notices,

in Bangladesh, that this empowerment might exclude men from the labor market.

Generally, women tend to be more conservative with their investment strategies (Ar-

mendáriz & Morduch, 2005, p.183), they are better at repaying their loans and more willing

to cooperate with their loan groups1. However women often act merely as intermediaries for

their family, meaning that men spend the contracted loan, while women are burdened with

the inherent risk. Thus, “women are kept out of waged work and [are] pushed into the informal

economy” (Cons and Paprocki, 2008).

1 Website of Grameen Bank, section: “About us – At a glance”, http://www.grameen-info.org, (Ac-

cessed on March 15, 2010).

6

2.1.2 Microcredit: a tool to alleviate poverty

Microcredit is an important component of microfinance. Latin America exemplifies well this

issue where high microcredit penetration rates stand; Peru, Paraguay and Chile have pene-

tration rates between 25% and 35%.1 The Foundation for International Community Assis-

tance (FINCA)2 defines microcredit as “the provision of working capital to fuel the productivity of

the world’s poor majority”. It is considered to be “a small amount of capital, typically $50 to

$300”3, but can be more consequent depending on the country. Microcredit is usually charac-

terized as a transaction where no collateral is usually provided by the borrower to the coun-

terparty, while the settlement period is typically a short one, e.g., 6-12 months with weekly

payments (Forum for the Future, 2007).

Conversely, microcredit is hard to be defined rigorously with regards to the size of the loan,

e.g., $300, $500 or $1,000. Berger & al. (2006, p.4) discard a strict microcredit threshold defini-

tion because of different levels of development, incomes, and prices existing across coun-

tries.

Moreover, microcredit loan portfolios are generally characterized by a low average loan bal-

ance, defined by the MIX as less than 250% of GNI per capita4. Table 2.1 provides figures in

regards to average loan sizes by region as of December 2009.

Table 2.1: Average loan balance per borrower by region

SSA EAP ECA LAC MENA SA All Regions

Avg. Loan Balance per Borrower

(in $)5

626 684 4008 1341 746 912 1588

Avg. Loan Balance per Borrower

(in % of GNI per Capita)6

138 48 155 47 44 115 97

Source: based on MIX (2010a)

1 Website of Microcredit Summit Campaign, section: “Blog – Small is beautiful for Latin American

(March 5, 2009)”, http://www.microcreditsummit.org (Accessed on March 15, 2010).

2 FINCA International is a non-profit microfinance organization, headquartered in Washington, D.C

Along with Grameen Bank and Acción International, FINCA is a leading microfinance organization.

3 Website of FINCA International, section: “Frequently Asked Questions”, http://www.finca.org (Ac-


4 Website of the MIX, section: “About- Microfinance”, http://www.themix.org (Accessed on March 15,

2010).

5 Is equivalent to “Adjusted Gross Loan Portfolio/Adjusted Number of Active Borrowers” (source:

website of MIX, http://www.themix.org, accessed on March 15, 2010).

6 Is equivalent to “Adjusted Average Loan Balance per Borrower/GNI per Capita”, where GNI per

Capita is the “Total income generated by a country’s residents, irrespective of location/ Total number

of residents” (source: website of MIX, http://www.themix.org, accessed on March 15, 2010).

7

Sheltered jobs and steady sources of income elude poverty. To get by, one can create and run

his/her own microenterprise. The latter may be small, but the cumulative impact is colossal.

Depending on the country, micro-enterprises employ 30 to 80% of the working population

(United Nations, 1997). Even if recent studies (e.g., Roodman & Morduch, 2009) raise doubts

about the poverty impact of microcredit (and generally microfinance), its objective remains

to “enable people to work their way out of poverty”.1

As a weapon for fighting poverty in the developing world, microcredit is as vital as education,

health care, human rights and stable government (Smith & Thurman, 2007). To emphasize

its importance in fighting poverty, the United Nations declared 2005 the International Year of

Microcredit2. This associates with the Millennium Development Goals, where one of the pur-

poses by 2015 is to decrease by 50% the proportion of people living currently in extreme

poverty.

Microcredit is crucial in order to grasp a better understanding of the transition process to

sustainability for the microfinance area since this, in the long-run, might be an excellent ap-

proach for any “practitioner of development and for those eager to change the way financial institu-

tions, international agencies and private actors service poor populations throughout the world”

(UNCDF, 2005). The essence and driving force of microfinance is to create an environment

for development and independency for low-income people and, in a wider perspective, for

nations.

2.2 Microfinance institutions: the micro level

Schumpeter (1911) argued that advanced services provided by financial intermediaries - like

mobilization of savings, allocation of capital, management of risk, transaction facilities and

firm monitoring- are indispensable for economic growth and development. Hence appropri-

ate financial intermediaries might play a central role in the developing world by providing

financial services that “stimulate economic growth by increasing the rate of capital accumulation

and by improving the efficiency with which economies use that capital” (King & Levine 1993,

p.735).

2.2.1 Definition and overview

A microfinance institution (MFI) is an organization that provides financial services to poor

and low-income clients who are not served by mainstream financial service providers (Mers-

1 Refer for instance to CGAP (2010a) for a deeper discussion.

2 Website of International Year of Microcredit, http://www.yearofmicrocredit.org (Accessed on March

15, 2010).

8

land & Strøm, 2009). MFIs play a

well positioned in reaching out to low

financial and socioeconomic development.

area with acceptance amongst the local population

regards to the local context and

clientele”2. Besides this, performing assets

Thus financial institutions providing mainly consumer

people, are not considered MFIs

In order to define the microfinance industry reliable and comprehensive data is essential;

however these data are difficult t

cording to Daley-Harris (2009) 3’552 MFIs worldwide reported reaching approximately 155

million microfinance clients as of December 31, 2007, with 83.4% of them being women.

Another source of information is the Microfinance Information eXchange (MIX)

tion, which merged three databases

coverage. Combining the three sources, 2’420 MFIs reported reaching 99.4 million microfi

ance clients in 117 countries. Most MFIs in this sample are concentrated in South Asia and

Sub-Saharan Africa, while most borrowers are concentrated in South Asia, East Asia and the

Pacific region (MIX, 2008).

Figure 2.2: Regional distribution of microfinance clients

1 “Financial inclusion may be defined as the process of ensuring access to financial services and timely and

adequate credit where needed by vulnerable groups such as weaker sections and low income groups at an affor

able cost.”(Rangarajan Committee,

2 Website of National Bank for Agriculture and Rural Development


3 The MIX is a non-profit organization t

industry.

4 MIX Market and the MicroBanking Bulletin, the Microcr

American Development Bank.

play a significant role in facilitating financial inclusion

ed in reaching out to low-income people. MFIs are important contributors to

nancial and socioeconomic development. Many of these institutions evolve

amongst the local population, have a greater awareness

and “have flexibility in operations providing a level of comfort to their

performing assets of MFIs are mainly microfinance financial servi

institutions providing mainly consumer loans, even if solely

re not considered MFIs (ResponsAbility, 2006).


however these data are difficult to establish, particularly regarding market penetration. A

Harris (2009) 3’552 MFIs worldwide reported reaching approximately 155


ation is the Microfinance Information eXchange (MIX)

, which merged three databases4 in order to provide comprehensive figures on market

coverage. Combining the three sources, 2’420 MFIs reported reaching 99.4 million microfi

17 countries. Most MFIs in this sample are concentrated in South Asia and

Saharan Africa, while most borrowers are concentrated in South Asia, East Asia and the

Regional distribution of microfinance clients Figure 2.3: Regional distribution of MFIs

Source: own research, based on MIX (2008)

defined as the process of ensuring access to financial services and timely and

adequate credit where needed by vulnerable groups such as weaker sections and low income groups at an affor

(Rangarajan Committee, 2008)

r Agriculture and Rural Development, http://www.nabard.org

profit organization that aims to promote information exchange in the microfi

MIX Market and the MicroBanking Bulletin, the Microcredit Summit Campaign, and the Inter

inclusion1, as they are

MFIs are important contributors to

evolve in a specific

awareness of the issues in

have flexibility in operations providing a level of comfort to their

are mainly microfinance financial services.

, even if solely to low-income


establish, particularly regarding market penetration. Ac-

Harris (2009) 3’552 MFIs worldwide reported reaching approximately 155


ation is the Microfinance Information eXchange (MIX)3 organiza-

in order to provide comprehensive figures on market

coverage. Combining the three sources, 2’420 MFIs reported reaching 99.4 million microfin-

17 countries. Most MFIs in this sample are concentrated in South Asia and

Saharan Africa, while most borrowers are concentrated in South Asia, East Asia and the

Regional distribution of MFIs

defined as the process of ensuring access to financial services and timely and

adequate credit where needed by vulnerable groups such as weaker sections and low income groups at an afford-

http://www.nabard.org (Ac-

exchange in the microfinance

edit Summit Campaign, and the Inter-

9

As the MIX (2008) disclaimer advises, these numbers should not be considered exact repre-

sentations of the global figures. The statistics correspond to a sample of MFIs that self-

reported their figures to the MIX. MFIs that voluntarily provide their information tend to be

more efficient and well-managed than the majority of MFIs; subsequently the aforemen-

tioned numbers are not perfectly accurate. This discrepancy might be explained by a signifi-

cant number of informal operators characterizing the field of microfinance.

2.2.2 Microfinance institutions in the landscape of financial service providers

The organizational structure and management in combination with the degree of oversight

of supervision by the government determines the institutional formality of MFIs (CGAP,

2006).

Figure 2.4: The spectrum of financial services providers

Source: CGAP (2006, p.36)

Low-income people largely obtain financial services through informal arrangements. Ar-

rangements may well be made amongst friends and family, or with saving collectors, shop

keepers, and moneylenders. Often despised for exploiting low-income people, moneylend-

ers in fact “offer a valued financial service in many communities” (CGAP 2006, p.37).

Cooperative financial institutions are member-based organizations, owned and controlled by

their members. Financial cooperatives are usually not regulated by a governmental banking

supervisory organism, but they may be supervised by a national or regional cooperative

council. Financial cooperatives are generally non-profit institutions.

10

Non-governmental organizations (NGOs) have been the true pioneers of microfinance. Ac-

cording to CGAP (2006), at least 9000 NGOs are providing financial services. NGOs may

face constraints in the range of financial services that they are authorized to provide; e.g.,

NGOs may not be allowed to offer deposits-taking services. Most of Latin American MFI

pioneers began as NGOs, working in urban markets. They have focused on microcredit as

their primary service offering, and only recently began to develop their product range

(Berger & al. 2006, p.41).

The existence of microfinance is owed to the lack of ability or inclination of formal financial

institutions to serve the unbanked and under-banked people. On the other hand, these insti-

tutions have the means to make the financial system truly inclusive. CGAP (2006, p.49) con-

siders state-owned banks as “immense sleeping giants [that] could play a big role in scaling up

financial services for the poor”.

Amongst private commercial banks four types of institutions can be distinguished:

� Rural banks have emerged in specific countries. They target clients in non-urban

areas generally involved in agricultural activities.

� Non-bank financial institutions (NBFIs) include both for-profit and non-profit or-

ganizations. A separate license for NBFIs may exist in return for being allowed to as-

sume additional roles, including, for some, taking deposits (Cull, Demirguç-Kunt &

Morduch, 2008). NBFIs encompass mortgage lenders, consumer credit companies,

insurance companies, and certain types of specialized MFIs.

� Specialized microfinance banks entail transformed NGOs, NBFIs, and banks that

from their establishment were entirely dedicated to microfinance.

� Commercial banks are fully licensed financial institutions regulated by a state bank-

ing supervisory agency (CGAP, 2006). Commercial bank MFIs are likely to be for-

profit and rely to a larger extent on commercial funds (both debt and equity funding)

and deposits. This category consists of microfinance banks, with microfinance as

their main activity, as well as a number of commercial banks, who established spe-

cialized microfinance departments within their operations in order to focus on poor-

er target groups.

2.2.3 Specific features and lending methodologies of microfinance institutions

The contrasts between MFIs and the mainstream financial institutions are important to be

mentioned at this stage. Honohan (2005, chap. 3) provides three main characteristics that

differentiate microfinance from mainstream financial institutions: scale, subsidy and style of

operation.

11

Scale can be perceived as a transitional phenomenon

a function of scale. Rather than having a large number of

institutions, “seems to be the key to ensuring that th

population” (Honohan 2005, p.

ing scale when region-wide economic

poverty. Hence small-scale, informal

are “unable to dissipate the risk through pooling

2005, p.13).

Style of operation differs between microfinance and the main

ize the diversity within microfinance itself.

tic of an MFI. Cull, Demirgüç

ing methodologies for providing microcre

lending-based arrangements.

The individual lending method applies to MFIs that

a lender and a single borrower. Solidarity group lending

tracts between a lender and a

but the group is confronted with

thodology applies to institutions that

cipatory lending by forming a single branch

finance movement, the practice of group lending

emphasis from academics seeking to

enforcement and administrative costs

Figure 2.5: Process of group

a transitional phenomenon and the sustainability of

ather than having a large number of MFIs, achieving scale of individual

seems to be the key to ensuring that the sector has reached a large proportion of the

2005, p.12). Moreover, one has to emphasize the importance of achie

economic shocks occur. The latter can plunge households into

informal and geographically confined financial arrangements

the risk through pooling” and (geographical) diversification

between microfinance and the mainstream. It is important to

ize the diversity within microfinance itself. The lending methodology is a

Cull, Demirgüç-Kunt & Morduch (2007, 2009) distinguish between three

ing methodologies for providing microcredit; the individual methodology

The individual lending method applies to MFIs that use standard bilateral contracts between

a lender and a single borrower. Solidarity group lending applies to institutions that use co

a solidarity group of borrowers. Loans are made to

but the group is confronted with a joint liability for repaying the loan. The village bank

thodology applies to institutions that offer large groups the opportunity to engage in part

lending by forming a single branch. Being part of the major innovation

he practice of group lending (Figure 2.5) in particular has received great

seeking to comprehend how microfinance deals with in

enforcement and administrative costs (Honohan 2005, p.15).

Figure 2.5: Process of group-lending-based contracts

Source: Dieckmann (2007)

and the sustainability of an MFI is partly

chieving scale of individual

e sector has reached a large proportion of the

he importance of achiev-

plunge households into

and geographically confined financial arrangements

and (geographical) diversification (Honohan,

t is important to real-

The lending methodology is a major characteris-

between three lend-

dit; the individual methodology and two group-

use standard bilateral contracts between

applies to institutions that use con-

are made to individuals,

for repaying the loan. The village bank me-

offer large groups the opportunity to engage in parti-

major innovation of the micro-

in particular has received great

microfinance deals with information,

12

For instance, Armendáriz & Morduch (2005, chap. 4) mention that group-lending-based con-

tracts provide, in principle, efficient outcomes through the promotion of social capital, even

without collateral. Moreover, group lending mitigates problems created by adverse selec-

tion (Morduch, 1999), and “ensures low default rates and replaces standard collateral” (Dieck-

mann, 2007, p.4).

Other features that differentiate MFIs from mainstream institutions in regards to the style of

operation include: the progressive increase in the amount borrowed from an individual or

group members as each successive loan is repaid, the use of non-traditional collaterals (e.g.,

T.V.) and the high frequency of required repayment installments (Honohan, 2005, p.16).

Subsidy: A large portion of MFIs may benefit from subsidies, whether in the form of tech-

nical support, a donation of capital, which is not expected to be compensated, or a flow of

funds provided at below market rates. Overall, MFIs remain heavily granted and subsidy-

dependent (Honohan, 2005). The subsidy feature through donation is further analyzed in the

following section considering the source of funding of MFIs.

2.3 Microfinance funding environment

Currently, microfinance is not considered anymore as an isolated marginal sector that needs

to be served only by niche market MFIs. Microfinance is becoming an integrated segment of

the broader financial system. The example of the Mexican MFI Compartamos depicts well

this evolution when, in April 2007, it sold 30% of its shares in an initial public offering

(IPO)1, oversubscribed 13 times and netted approximately US$467 million for the original

investors (Daley-Harris, 2009). The success of the Compartamos IPO will no doubt facilitate

future funding of MFIs, and improve microfinance image, particularly in regards to cross-

border investors (CGAP, 2007a).

2.3.1 Sources of funding for microfinance institutions: an overview

The availability of capital is a key factor for the growth of an MFI (Krauss & al., 2007, p.3)

and it cannot be met by donor funds or philanthropists alone. In order to supply microfin-

ance borrowers with its services, an MFI needs capital on the liability side of its balance

sheet. The funding process follows the same principle like a mainstream financial institution.

In addition to deposits, an MFI may be financed with debt capital, and to some extent with

equity. The equilibrium between debt and equity financing is key to the development and

1 IPO means the first sale of stock by a private company to the public. “IPOs are often issued by smaller,

younger companies seeking the capital to expand, but can also be done by large privately owned companies look-

ing to become publicly traded.” http://www.investopedia.com (Accessed on March 15, 2010).

13

growth of an MFI (Maisch & al. 2006). Appendix II provides the pros and cons of each capi-

tal structure. Microfinance has an estimated demand for capital of US$ 270 billion (Forum

for the Future, 2007), where $45 billion might be provided in equity and US$ 225 billion in

debt, assuming a 5:1 leverage ratio as the current global benchmark1.

From the perspective of a microfinance investor, equity investment might be more likely in

an MFI with a high growth potential over the medium-term and with a high gross margin to

sustain its cash flow. MFIs that do not match the aforementioned criteria may still be poten-

tial equity investment candidates, although “with an investment structured to have lower risk”.

(Maisch & al. 2006, p.80).

Latin American MFIs have to a large extent a diversified source of funding. Based on a sam-

ple of 42 MFIs from the Latin America and Caribbean regions, as of June 2008, MicroRate

(2009a, p.30) finds that domestic source -including deposits, local commercial loans and oth-

er domestic debt capital sources- make up for 59% of funding of MFIs. Equity, stemming

from both domestic and international sources, accounts for another 30%. International

source of funding through debt accounts for the remaining part (11%).

Globally, Cull & al. (2008) find that microfinance banks (the more formalized institutions)

rely predominantly on commercial funding and deposits. NGOs (app. 40% of the sample)

rely mainly on donations and non-commercial borrowing. Credit unions (member-based

financial institution) rely predominantly on deposits provided by their own members.

Table 2.2: Shares of total funding by institutional type (2005-2007)

Donations Non-commercial

borrowing

Equity Commercial

borrowing

Deposits

Bank 2% 1% 13% 13% 71%

Credit Union 11% 3% 16% 6% 64%

NBFI 23% 11% 18% 28% 21%

NGO 39% 16% 8% 26% 10%

Total 26% 11% 13% 23% 27%

Source: own representation based on Cull & al. (2008)

1 Leverage Ratio = Debt/Equity = (Assets – Equity)/Equity = (60.565 – 9.936)/9.936 ≈ 5 (MIX, 2010a).

14

2.3.2 Degree of commercialization and

The funding situation of an MFI is associated with its

mercialization refers to the transition from a state of

dized operations into one in which

are part of the formal financial system

(Figure 2.5) according to their degree of commercialization

that reigns amongst MFIs. Meehan (2004, p.

larger more commercially oriented specialized MFIs, many of whom are, or intend to become, reg

lated financial intermediaries, and smaller, NGO

Figure 2.5: Types of MFIs according to their degree of commercialization

Source: Dieckmann (2007) based on

Tier 1 MFIs are developing into formal financial institutions, and

the attention of private and institutional investors. Typicall

a more experienced management team, and are regulated institutions.

structure is composed of deposits, debt and equity (

2 MFIs are smaller and less mature

are predominantly NGOs that are in the process of transforming into regulated MFIs. Tier

MFIs may receive funding from pu

Moreover, their capital structure is less complex than tier 1 MFIs and mainly

debt (BlueOrchard, 2009). Tier 3 MFIs are predominantly

are close to becoming profitable MFIs, but are characte

Lastly, tier 4 MFIs are start-ups or informal financial institutions for whom

not their primary focus (Dieckmann,

f commercialization and issues

an MFI is associated with its degree of commercialization.

transition from a state of heavily donor-dependency

dized operations into one in which MFIs are financially self-sufficient and sus

financial system (Ledgerwood & al. 2006). A classification of MFIs

according to their degree of commercialization depicts the g

. Meehan (2004, p.7) states, “a growing divide is emerging between

larger more commercially oriented specialized MFIs, many of whom are, or intend to become, reg

lated financial intermediaries, and smaller, NGO-managed MFIs”.

Types of MFIs according to their degree of commercialization

Source: Dieckmann (2007) based on Meehan (2004)

Tier 1 MFIs are developing into formal financial institutions, and are increasingly attracting

and institutional investors. Typically, tier 1 MFIs are profitable, have

a more experienced management team, and are regulated institutions. T

deposits, debt and equity (BlueOrchard, 2009). On the

are smaller and less mature MFIs. According to Dieckmann (2007), these

are predominantly NGOs that are in the process of transforming into regulated MFIs. Tier

MFIs may receive funding from public or institutional investors, but less than Tier 1 MFIs.

Moreover, their capital structure is less complex than tier 1 MFIs and mainly

Tier 3 MFIs are predominantly NGOs as well. These institutions

to becoming profitable MFIs, but are characterized by a lack of

ups or informal financial institutions for whom

mann, 2007).

degree of commercialization. Com-

dependency of subsi-

sufficient and sustainable, and

A classification of MFIs

the growing disparity

“a growing divide is emerging between

larger more commercially oriented specialized MFIs, many of whom are, or intend to become, regu-

Types of MFIs according to their degree of commercialization

are increasingly attracting

are profitable, have

Tier 1 MFIs capital

On the contrary, tier

), these institutions

are predominantly NGOs that are in the process of transforming into regulated MFIs. Tier 2

ess than Tier 1 MFIs.

Moreover, their capital structure is less complex than tier 1 MFIs and mainly composed of

. These institutions

sufficient funding.

ups or informal financial institutions for whom microfinance is

15

Consequently, MFIs have an incentive to upgrade their institutional and regulatory status

(e.g., from tier 2 to tier 1) in order to access more capital. This need for commercialization of

MFIs, other than the increase in their depth of outreach1, is prompted by an endeavor for

growth. Looking from a socially-motivated international investor viewpoint, some remarks

have to be mentioned at this point.

An issue that can arise from this search of financial expansion through commercialization is

a phenomenon called mission drift, which describes the process whereby an MFI departs

from its social mission, and increasingly focuses on its financial performance. Mission drift

occurs as an MFI might find more profitable to reach out to wealthier clients while crowding

out poorer clients.

The risk of mission drift is more likely when an MFI “transforms into a formal institution or

when shareholders are changing” (Lapenu & Pierret, 2005, p. 67). As such, the commercializa-

tion of an MFI is expected to harm its social performance, consequently deteriorating the

dual return that foreign institutional investors expect to achieve from the financial and social

performance of the MFI invested in (Mersland & Strøm, 2009). From a policy viewpoint,

Armendáriz & Szafarz (2009) emphasize that “donors and socially responsible investors can be

easily mislead by MFIs which are serving unbanked wealthier populations”.

In addition, MFI growth can be sustainable and reflect financial strength, but uncontrolled

growth can be hazardous for an MFI. It can lead to increasing delinquencies and, in the me-

dium-term, problems that can even result in an MFI bankruptcy (Lapenu & Pierret, 2005).

This issue of uncontrolled growth is reflected in recent delinquency crises in Nicaragua, Mo-

rocco, Bosnia and Herzegovina, and Pakistan (CGAP, 2010b).

Past crises (e.g., East Asian and Bolivian one) in developing and transition economies have

generally supported the argument of counter-cyclicality in microfinance (e.g., Krauss & Wal-

ter, 2008). However, it is expected that microfinance might be more affected by economic

downturns than in the past (Fitch Ratings, 2008).

First, a challenge is particularly faced by tier 1 and 2 MFIs. As an MFI transforms and com-

mercializes, and as microfinance borrowers are becoming integrated into the mainstream

financial system, a risk that can occur is that “the resulting convergence between microfinance

and mainstream banking effectively strips microfinance of the very characteristics that help to insu-

late it to some extent from wider economic trends.” (Fitch Ratings, 2008, p.17).

1 As already mentioned in the first section of this chapter, MFIs have generally been developed to

reach a population excluded from the mainstream financial system. Outreach refers to the ability of

an MFI to reach large number of clients. The depth of outreach of an MFI can be measured “to eva-

luate its focus on the economically and socially excluded population” (Zeller & al. 2003, p.5).

16

Second, MicroRate (2008a, p.14-15) notes that the definition of “microcredit” has evolved

throughout the years to actually include a broader range of forms of lending to low-income

people, not considered by microfinance in the past; e.g., consumption loans and small-

business loans. MFIs that provide low-income people with microcredit in order to create

wealth might not be affected by an economic recession. On the other hand, MFIs that lend for

other needs (particularly consumption) and provide small businesses with “microcredit”

might be more exposed to an economic downturn.1

To conclude, “MFIs that have strayed over the boundary that divides microcredit from consumer, or

small lending will be more vulnerable than those MFIs that remain focused on core microfinance ser-

vices.” (MicroRate, 2008a, p.XI).

1 MicroRate (2008a, p.XI) argues: since small businesses, contrasting with micro-enterprises, often

carry sizable fixed assets, the former might be “highly vulnerable when the economy contracts.”

17

3. International funding of microfinance

This chapter provides a framework of international key players funding microfinance. Furthermore, it

aims to depict how flows of cross-border funding reach MFIs, by distinguishing primary investors

(section 3.1) from intermediary investors (section 3.2). Therefore, the present chapter is structured so

as to position microfinance investment vehicles in a clarified microfinance investment landscape.

3.1 Primary cross-border funders

3.1.1 Classification of primary funders

The landscape of primary cross-border funders1 in microfinance is categorized in two

groups: donors and investors. Table 3.1 provides a comprehensive classification.

Table 3.1: Landscape of primary cross-border funders

Donors Investors2

Bilateral Agencies

Aid agencies and ministries of governments in

developed countries [e.g., Swedish International

Development Agency (Sida), United States Agen-

cy for International Development (USAID)]

International Financial Institutions (IFIs)

The private sector arms of government-owned

bilateral and multilateral development agen-

cies [e.g., KfW (Germany), IFC, European In-

vestment Bank (EIB)]

Multilateral Development Banks & UN Agencies

Agencies owned by multiple governments of the

industrialized and developing world [e.g., World

Bank, regional development banks], and UN

agencies [e.g., the United Nations Capital Devel-

opment Fund (UNCDF), International Fund for

Agricultural Development (IFAD)]

Individual Investors

Socially-motivated individual, “retail” inves-

tors and high net worth individuals that act as

venture philanthropists. Individual investors

provide their capital through organizations

like Oikocredit, a Dutch cooperative society,

investment funds, and peer-to-peer platforms.

Foundations

Non-profit corporations or charitable trusts typi-

cally funded by a private individual, a family or a

corporation, with a principal purpose of making

grants to unrelated organizations [e.g., Bill and

Melinda Gates Foundation, Ford Foundation]

Institutional Investors

International retail banks, investment banks,

pension funds, and private equity funds that

channel capital into microfinance, often with

an expectation of return that is below market

[e.g., Deutsche Bank, TIAA-CREF]

International NGOs

Non-governmental organizations that can be ei-

ther specialized in microfinance [e.g., ACCION,

FINCA] or work in multiple sectors, including

microfinance [e.g., CARE, Concern Worldwide]

Source: adapted from cgap.org and CGAP (2009a)

1 Littlefield & al. (2007): “Primary funders” stand for those with both ownership and decision-making

over funds, and other intermediary structures. The latter is tackled in section 3.2.

2 In this thesis, the term “investors” is used for both lenders and equity investors.

18

3.1.2 Global overview and actual issues of international funding

Developing and transition economies receive international funding for microfinance. Tradi-

tionally, MFIs have been funded mainly from international financial institutions (IFIs),

NGOs, charities, foundations and other donors. Donors may get involved in MFIs through a

wide range of functions; policy support, technical assistance, grants, loans1, quasi-equity2,

equity investments in MFIs that can sell shares, and guarantees. For donors, direct funding

of MFIs might be the most effective channel (CGAP, 2006, p.95). However, many donors, par-

ticularly multilateral development banks, work only with governments, typically providing

them with soft loans. The latter might be suitable for funding traditional aid activities (e.g.,

building roads, hospitals, and schools), but less appropriate for supporting MFIs develop-

ment (CGAP, 2006). On the one hand, Dunford (2003) argues that healthy development of

microfinance might not be reached through a flow of institutional investments, but through

a network of retail delivery channels financed predominantly by IFIs and donors. On the

other hand, there is a widespread recognition that on the long-term neither IFIs nor donors

(e.g., NGOs) are successful in delivering sustainable services to significant numbers of MFIs

(Honohan, 2005).

A shift in direct cross-border funding is occurring; institutional and (for-profit) individual

investors are progressively filling this role of sustainable investor (Berger & al. 2006). Be-

sides, microfinance is increasingly recognized as an (emerging) asset class among global

private investors. Microfinance investments offer a double-line return – a financial and a so-

cial one. In addition, investing in microfinance may provide portfolio diversification value

for international investors (Krauss & Walter, 2008). Currently, foreign sources account for

15% of microfinance funding, while domestic sources of funding, including deposits, ac-

count for 85% (CGAP, 2009b); in LAC region, foreign sources of funding might account for

slightly more, i.e., approximately 20% (MicroRate, 2009a). As of December 2008, microfin-

ance funders (i.e., donors and investors) disbursed US$ 3 billion and increased their com-

mitments to microfinance by 24%, reaching approximately US$ 14.8 billion committed3,

whereas 84% of the later amount is intended for funding the micro level (i.e., MFIs), directly

or indirectly through intermediaries (CGAP, 2009a, p.5). The remaining funding is provided

to support financial market infrastructures (meso level) and policy, regulatory and supervi-

sory organisms (macro level). Commitment at the policy level requires less capital than fi-

nancing large and emergent MFIs, thus the funding repartition is coherent4.

1 e.g. loans which are offered at subsidized or commercial interest rates (CGAP, 2006).

2 e.g. low-interest loans that can be converted into equity (CGAP, 2006)

3 Annual committed figures don’t translate neatly to disbursed amounts to microfinance, e.g. between

20% and 70% committed to microfinance get actually disbursed by donors (Littlefield & al. 2007).

4 Website of CGAP, section: “Global Estimates - Microfinance Donors & Investors”,


19

Globally, for the first time, investors account for more than half of the total commitment,

while donors complete the rest of the aforementioned amount committed (Figure 3.1). D

spite the financial crisis, funding pro

have not been affected (CGAP, 2009


South Asia and Sub-Saharan

and Latin America rely predominantly on investors rather than d

border funding is heavily concentrated in certain countries,

total funding (i.e., investors and donors) flows only to 5 out of

Mexico, Ecuador, Bolivia and

Figure 3.2 provides a regional

annual growth rates by region

Figure 3.2

1 This second annual CGAP funder survey

represent an estimated 80% of the funding to microfinance (CGAP, 2009


while donors complete the rest of the aforementioned amount committed (Figure 3.1). D

funding projections for 2009, reported by a majority of funders,

have not been affected (CGAP, 2009a)1.


Source: CGAP (2009a)

Africa depend mainly on donors. Conversely

and Latin America rely predominantly on investors rather than donors.

concentrated in certain countries, e.g., in LAC region, 50% of the

investors and donors) flows only to 5 out of 23 countries,

Mexico, Ecuador, Bolivia and Nicaragua (CGAP, 2009a).

regional breakdown of the total amount committed

by region.

Figure 3.2: Committed amount (US$ million) by region


This second annual CGAP funder survey includes responses from 61 donors and investors that

of the funding to microfinance (CGAP, 2009a).


while donors complete the rest of the aforementioned amount committed (Figure 3.1). De-

, reported by a majority of funders,

onversely Eastern Europe

onors. In addition, cross-

in LAC region, 50% of the

23 countries, namely Peru,

committed and respective

responses from 61 donors and investors that

20

Regions worldwide have seen their respective committed amounts increasing, except from

MENA region (-5%, CGAP 2009a); this might be primarily attributed to a relative small in-

ternational funding amount in this region that grew excessively between 2005 and 2007,

coupled with an excessive concentration of funding sources in 2007, i.e., the top-five funders

in 2007 represent almost 70% of total funding (53.5% in 2008) in MENA (CGAP, 2008a).

Specifically, the major funder in 2007 in MENA, the European Investment Bank (EIB)1 might

have partially withdrawn in 2008 its commitment to Moroccan microfinance (cf. EIB, 2008),

in order to focus on larger-scale projects and support modernization programs within the

same country; e.g., construction of motorway2.

At present, it might be premature to attribute causality between the reduced growth of fund-

ing in MENA region3 and recent delinquencies in Moroccan MFIs that lead to a regional mi-

crofinance crisis (e.g., CGAP, 2010b). However, the IFIs proactiviness towards such pheno-

mena is questionable4.

Paradoxically, until recently, IFIs have largely preempted institutional and individual inves-

tors from entering the rapidly growing Moroccan MFI market, by offering terms which insti-

tutional and individual (private) investors cannot match (Abrams & von Stauffenberg, 2007,

p.14, cf. MFI Al Amana).

In the last several years, the rapid growth of institutional and individual investments to

MFIs has led to a role reversal5 between IFIs and private investors. Namely, IFIs are concen-

trating their loans in the top-tier MFIs (cf. Figure 2.5), leaving private investors to look for

opportunities among smaller, riskier MFIs. Consequently, IFIs are crowding private investors

out of the top-tier MFIs (Abrams & von Stauffenberg, 2007, p.3). On the other hand, for a

matter of relativism, one should stress that IFIs “have played a vital and powerful role in the

recent acceleration of microfinance, for which everyone in the [microfinance] community is grate-

ful.” (Microfinance Gateway, 2007, p.5). Therefore, the general consensus might be the fol-

lowing:

1 “EIB is the European Union's (EU) long-term lending institution established in 1958 under the Treaty

of Rome and owned by EU member states, who subscribe to its capital EUR 164 billion. EIB supports

projects within its member states, and finances investments in future member states of the EU and EU

partner countries (e.g. Morocco). The EIB operates on a non-profit maximizing basis and lends at

close to the cost of borrowing” (adapted from source: website of EIB, section: “About”,

http://www.eib.org (Accessed on March 15, 2010).

2 Website of EIB, section: “Projects”, http://www.eib.org (Accessed on March 15, 2010).

3 Moroccan MFIs are preponderant in MENA region, where Morocco and Egypt receive 77% of fund-

ing committed to MENA (CGAP, 2009a).

4 According to CGAP (2009c), the causes of the Moroccan microfinance crisis might be unsustainable

growth.

5 cf. Abrams & von Stauffenberg (2007). The paper focuses stricto sensu on direct lending to MFIs; e.g.

equity, guarantees or investments through microfinance investment intermediaries are not addressed.

21

a) IFIs should invest in lower-tier MFIs, which private investors are unwilling or unable to

consider (IAMFI, 2009, p.45).

b) IFIs should exit an MFI investment once the latter achieves its sustainability, and private

investors are ready to invest in it (IAMFI, 2009), and seed the next generation of MFIs (Abrams

& von Stauffenberg, 2007, p.17).

c) IFIs should enhance catalytic investments that attract institutional and individual inves-

tors, especially in an economic downturn (IAMFI, 2009, p.45), by making their funding more

transparent (Abrams & von Stauffenberg, 2007).

In 2008, IFIs, particularly KfW and the International Finance Corporation (IFC), still domi-

nate the scene, owning over half of the total outstanding portfolio1 (CGAP, 2009b). In con-

trast to most of the other microfinance funders, IFIs tend to provide considerable financing

directly to retail MFIs; two-third of their funding is provided by IFIs (CGAP, 2009a).

IFI portfolios are set to keep climbing, but institutional investors have shown growing inter-

est in microfinance investments, especially from 2006 onwards.

Figure 3.3 presents the historical growth and actual breakdown of funding by type of inves-

tor.

Figure 3.3: Microfinance investment growth by investor type

Source: own research, adapted from CGAP (2009b) and cgap.org

1 Outstanding Portfolio = Funds disbursed minus repayments (CGAP, 2009a).

IFIs

49%

Microfinance Investors 2008

Institutional Investors29%

Individual Investors22%

1.7

2.4

4.1

5

0.5

1.1

2.2

2.9

0.8

0.9

1.6

2.2

0

1

2

3

4

5

6

2005 2006 2007 2008

Investments in Microfinance (US$ billions)

22

Aggregate institutional investor portfolio has grown from 17% of total microfinance invest-

ment in 2005 to 29% in 2008. Institutional investment is mainly composed by 13 commercial

banks (aggregate assets of US$ 797 mil.), 6 pension funds (aggregate assets of US$ 681 mil.)

and 5 private equity firms focusing on investments in India (CGAP, 2009b). Deutsche Bank,

Citigroup, HSBC, ING, and ABN Amro, for instance, invest in microfinance through direct

loans and other funding channels. Besides pure funding, international banks play a signifi-

cant role in training MFIs to mainstream financing techniques (CGAP, 2008b).

Furthermore, individual investors have made considerable investments in MFIs and micro-

finance networks. Nowadays, an individual investor can directly channel money to micro-

finance entrepreneurs through the internet lending platform Kiva1 that facilitated US$120

million worth of microcredits to approximately 320,000 entrepreneurs as of March 20102.

Nevertheless, individual investors predominantly invest in microfinance through invest-

ment funds and other investment intermediaries, rather than supporting MFIs directly (Lit-

tlefield, 2007). The microfinance investment intermediation aspect is further analyzed in the

following section.

3.2 Microfinance investment intermediaries

Presently, the microfinance area lacks an exhaustive classification of certain key elements

composing microfinance, e.g., investment intermediaries and financial instruments. This

might be attributed to the relative immaturity, constant evolution and complexity of micro-

finance, and probably to an actual lack of consensus among CGAP, leading asset managers,

academics and other industry experts. Recent classifications by CGAP present a compre-

hensible picture; however certain issues remain e.g., overlap between investor and donor

groups. This section intends to clarify microfinance investment intermediaries and sets up

the groundwork for chapter 4. 3

3.2.1 Definition of microfinance investment intermediaries

MIIs are “investment entities that have microfinance as one of their core investment objectives and

mandates” (CGAP, 2007b, p.5). They refer to a broad spectrum of players: MIVs (public and

private placement funds), holding companies, as well as other types of MIIs that provide

1 Kiva’s business model has been called into question recently. According to Strom (2009), the peer-to-

peer connection Kiva is offering to reach directly micro-entrepreneurs worldwide might be an illusion.

2 Website of Kiva, section: “About - Facts”, http://www.kiva.org (Accessed on March 15, 2010).

3 The present thesis adopts definitions and classifications in concordance with MicroRate (e.g. Micro-

Rate, 2009b). Besides, data from CGAP surveys (i.e. CGAP, 2009d and anterior) are incorporating MIIs

that are not in fact MIVs (e.g. ProCredit Holding AG). Therefore, sub-section 3.2.1 is adapted from

CGAP MIV framework, and sub-section 3.2.2 and chapter 4 follow MicroRate MIV framework.

23

directly or indirectly debt, mezzanine,

ever it must be noted that MIIs are not considered to be charities.

ent return expectations and risk tolerance

a profit (CGAP, 2007b).

MII stands for a generic denomination of a cen

investment vehicle (MIV, cf. sub

latter is slightly narrower than

According to CGAP (2009d),

bined assets under management

of total foreign investments in microfinance.

international investment flows; it is based on

investments data.


Source: own research, adapted from

3.2.2 Types of microfinance investment i

1) A microfinance investment v

adopt various legal forms (MicroRate,

funded. It must focus on microfinance

than 50% of its non-cash total

either be self-managed, or managed by an investment management firm or by trus

receive money from (or open to)

1 Cf. MicroRate (2009b, p.1)

mezzanine, equity, or guarantees to MFIs or to o

ever it must be noted that MIIs are not considered to be charities. Although t

risk tolerance, MIIs are all aiming at recovering their capital with

stands for a generic denomination of a central element of the thesis

, cf. sub-section 3.2.2 and chapter 4). However, the definition of the

latter is slightly narrower than the former.

, as of December 2008, 103 MIIs reported have

bined assets under management (AUM) of US$ 6.6 billion, which represents more than

vestments in microfinance. Figure 3.4 provides a simplified illustration of

flows; it is based on section 3.1 investor classification and primary


ource: own research, adapted from CGAP (2009b)

3.2.2 Types of microfinance investment intermediaries

icrofinance investment vehicle (MIVs) is an independent investment entity

(MicroRate, 2009b). Thus it must be independent of the MFIs being

microfinance as a “core investment objective and mandate

total assets are invested in microfinance (CGAP,

managed by an investment management firm or by trus

open to) multiple investors 1 “through the issuance of shares, units,

or to other MIIs. How-

Although they have differ-

aiming at recovering their capital with

tral element of the thesis, i.e., microfinance

However, the definition of the

have estimated com-

lion, which represents more than half

Figure 3.4 provides a simplified illustration of

ion 3.1 investor classification and primary

nvestment entity that can

dent of the MFIs being

objective and mandate”, i.e., more

(CGAP, 2009d, p.4). It can

managed by an investment management firm or by trustees and

through the issuance of shares, units,

24

bonds, notes or other financial instruments.”1 A financial vehicle “supported only by donors does

not qualify as a MIV.” (MicroRate, 2009c, p.17). Chapter 4 contributes to extend trends and key

issues in relation to MIVs.

2) Holding companies2 provide financing (chiefly with equity) and technical assistance to

MFIs and other non-specialized microfinance service providers that the holding company

owns, manages or controls. They usually hold a majority stake in their investees and are

generally accessible by private invitation only. ProCredit Holding, a German holding com-

pany that invests in lower-tier MFIs is a major participant in the microfinance area. The

holding has currently more than US$ 1 billion assets under management, making it the larg-

est actual MII (CGAP, 2009b). MFIs underlying holding companies, such as ProCredit banks

worldwide, are not independent entities in regards with the holding, thus the latter cannot

be considered as a stricto sensu MIV. A holding company structure doesn’t prevent the need to

ask shareholders for approval when exiting investments. Many MIV shareholders have a number of

holdings throughout the sector, [thus] creating conflicts of interest. (Unitus Capital, 2009, p.6).

3) Other types of MIIs may include but are not limited to:

a) Microfinance investment funds not open to multiple investors; e.g., Omidyar-Tufts Micro-

finance Fund, launched in November 2005 through a partnership between the Omidyar fam-

ily and Tufts University. The former donated to the latter US$100 million to launch and

manage the fund. Tufts receives half of the returns, as the other half is reinvested in the fund

to allow further microfinance investments3.

b) Investment entities not specialized in microfinance, but with a significant microfinance

investment portfolio, e.g., Calvert Foundation, established in 1995 in Bethesda, Maryland

and reported US$ 207 million in total assets in its 2008 annual report, where approximately

20% is committed to microfinance investments4.

c) Peer-to-Peer microlenders, e.g., Kiva (cf. sub-section 3.1.2).

Even if MIVs are relatively recent financial instruments, their framework is constantly evolv-

ing. The next chapter is grounded on the definition of MIV of the present section.

1 Website of CGAP, section: “Global Estimates - Microfinance Donors & Investors”,


2 Adapted from CGAP (2009d)

3 Website of Tufts University, section: “Microfinance Fund”, http://www.tufts.edu/microfinancefund

(Accessed on March 15, 2010).

4 Website of Calvert Foundation, http://www.calvertfoundation.org (Accessed on March 15, 2010).

25

4. Microfinance investment vehicles: a dual investment opportunity

This chapter depicts microfinance investment vehicles, their industry and actual trends.

4.1 Classifications: in constant evolution

Microfinance has grown from a community to an industry (Steidl, 2007, p. 111). Characteriz-

ing this transition, between 2004 and 2009, cross-border investment in MFIs has almost de-

cupled to reach approximately US $10 billion as of December 2009. This section examines the

flow of half of the aforementioned foreign capital through MIVs, which have evolved into a

major innovation for microfinance in the last decade. The first MIV guided by both financial

and social performance (double bottom lines) and not initiated by donors or IFIs is Dexia Mi-

cro-Credit Fund, launched in 1998 (CGAP, 2008b).

MIVs definition, which is tackled in section 3.2, encompasses numerous types of business

models “in terms of origin, investor base, philosophy, instruments, and targeted return rates”

(CGAP, 2008b, p.5), depending on a combination of investors’ profiles and objectives. In-

deed, as examined in section 3.1, participants investing in microfinance are very diverse.

According to the actual diversity of investors, MIVs may be classified in three different cate-

gories depending on their investment purpose1:

1) Commercial MIVs target primarily mainstream financial investors, both individual and

institutional. The objective of these MIVs is to provide social and financial returns through

investments in loans attributed to financially sustainable MFIs (i.e., top-tier). However, their

primary objective is a financial one, while social performance is a secondary one; e.g., Dexia

Micro-Credit Fund targets an annual return of “6-month Libor plus 1-2%.”2 There might be

more conservative investments as well, since financing MFIs through loans is a lower risk

exposure than equity financing. An interesting feature of these MIVs is that they strengthen

the requirements of transparency and clarity of information submitted by MFIs to MIVs.

Indeed, commercial MIVs “base their investment decisions on more formal criteria and strive to

raise the degree of transparency of their investments by requiring ratings or comprehensive financial

reports of MFIs.” (Dieckmann, 2007, p.12).

1 Adapted from Goodman (2006), Steidl (2007) and Dieckmann (2007).

2 Website of BlueOrchard, section: “Product & Services - Dexia Micro-Credit Fund (DMCF)”,

http://www.blueorchard.com (Accessed on March 15, 2010).

26

2) Quasi-commercial MIVs target mainly IFIs, donors and SRIs1, and are less transparent;

e.g., responsAbility Microfinance Leaders Fund is only open to selected qualified investors2.

Quasi-commercial MIVs aim to reach double bottom lines; hence they target financial re-

turns, but maintain a clear development mission (Steidl, 2007, p.116). As a result, they might be

satisfied with below market-based returns (e.g., LIBOR) (Dieckmann, 2007, p.12). Moreover, the

distinction between commercial and quasi-commercial MIVs is not an indication of perfor-

mance; some quasi-commercial MIVs have outperformed commercial ones, and the other

way around (Goodman, 2006, p.27). However, quasi-commercial MIVs can be a riskier in-

vestment than commercial MIVs, as the former holds in general more equity stakes than the

latter, i.e., higher risk exposure than loans.

3) Microfinance development vehicles are non-profit entities or cooperatives, that essential-

ly aim at social returns rather than financial ones, but maintain real inflation-adjusted fund

value if possible (Goodman, 2006, p.28), e.g., Oikocredit. Their primary objective is to make

capital available to MFIs to finance their growth. Microfinance development vehicles pro-

vide more favorable financing terms than the market, as well as subsidized technical assis-

tance to MFIs approaching financial sustainability (Dieckmann, 2007, p.12); however they nor-

mally do not provide them with grants or donations. Microfinance development vehicles are

the least regulated and transparent, but are complementary to the two previous categories,

i.e., by focusing on lower-tier MFIs, microfinance development vehicles would ground op-

portunities for further lower-tier MFI funding by commercial and quasi-commercial MIVs

(Goodman, 2006).

Popularly denominated as microfinance investment funds (MFIFs), until approx. 2006, MIVs

present a striking diversity in organizational structure and size. Only a minority are “funds”

in a narrower legal sense (MicroRate, 2006). The industry is relatively young and has not

settled on an established categorization in regards to MIVs’ organizational and legal fea-

tures. According to MicroRate (2009c), MIVs can exhaustively be categorized by their legal

structure, i.e., registered versus unregistered investment funds. Table 4.1 presents the latest

peer group classification by MicroRate3.

1 Cf. sub-section 4.1.2

2 Website of responsAbility, section: “Investment Products - responsAbility Microfinance Leaders

Fund”, http://www.responsability.com (Accessed on March 15, 2010).

3 MicroRate, Inc is a specialized rating agency for microfinance along with M-CRIL, Microfinanza and

PlaNet Rating. MicroRate provides specialized evaluation reports and independent ratings on MFIs

and MIVs. MicroRate produces due diligence reports and microfinance-related papers as well. Web-

site: http://microrate.com

27

Table 4.1: MIV peer group classification by legal structure

1) Registered Investment Funds are open to retail investors and are regulated by local mar-

ket authorities. They publish their net asset value on a regular basis.

MicroRate (2009c) lists 6 registered investment funds, among which:

� Dexia Micro-Credit Fund

� responsAbility Global Microfinance Fund

2) Unregistered Investment Funds

2.1) Collateralized Obligations offer investors two or more classes of investment (tranches),

each reflecting different levels of risk and return based on the cash flows of the underlying

portfolio. Usually structured as stricto sensu Collateralized Debt Obligations (CDOs), or Col-

lateralized Loan Obligations (CLOs).

MicroRate (2009c) lists 14 collateralized obligations, among which:

� BlueOrchard Microfinance Securities-1 (BOMS1)

� Global Partnerships Microfinance Funds

2.2) Private Investment Funds are open to qualified, accredited investors seeking a return.

As private companies, they are typically not subject to regulation by local market authorities

and are not open to retail investors.

MicroRate (2009c) lists 38 private investment funds, among which:

� Unitus Equity Fund

� The Dignity Fund LP

2.3) Not-for-Profit Investment Funds are non-profit organizations, including NGOs and

cooperatives, which reinvest most or all returns. These private organizations are typically

exempt from regulation by local market authorities.

MicroRate (2009c) lists 10 not-for-profit investment funds, among which:

� Oikocredit

� ACCION Gateway Fund

Source: own research, adapted from MicroRate (2009c, p.14-19) and CGAP (2009d, p.32)

28

Investments from surveyed MIVs (681 out of 74 identified) are totaling more than US$ 5 bil-

lion as of December 31, 2008, while almost US$ 4 billion is dedicated to microfinance with

2’826 positions in MFIs for an average investment size of US$1.4 million. Private investment

funds are the largest group in number of MIVs (cf. Table 4.1) and total microfinance assets

(US$ 1.382 billion, 35% of total), followed by collateralized obligations (US$ 1.003 billion,

26%), registered investment funds (US$ 781 million, 20%) and not-for-profit investment

funds (US $751 million, 19%). (MicroRate, 2009c).

4.2 Actual trends and implications for microfinance

Despite a growth deceleration, mainly attributed to the financial crisis economic downturn,

microfinance investments demonstrate a continued expansion and potential (LuxFLAG,

2009). Indeed, MicroRate (2009b, p.3) identifies 10 new MIVs launched (or to be launched) in

2008 and a potential increase of their investments by at least US$ 0.7 to 1.3 billion during

2009; e.g., responsAbility Global Microfinance Fund, the fourth largest MIV, reports an in-

crease in assets of US$ 123 million from January through December 2009. Moreover, CGAP

(2009e, p.8) forecasted for 2009 a market growth in terms of total MIV assets of 29%. This

could be principally driven by an increasing interest of individual and institutional inves-

tors, particularly socially responsible investors (SRIs)2, both in Europe and in the U.S.

(CGAP, 2008b).

However, the actual economic downturn is reflected by significantly lower MIV growth rate

(MicroRate, 2009b, p.1). Compared to an average MIV asset growth of 80% from 2005 to 2007

(cf. Figure 4.1), MicroRate (2009b) reports a growth of merely 31% from 2007 to 2008, i.e.,

from US$3.8 billion by the end of 2007 to US$5.04 billion by the end of 2008. Accounting for

80%, total outstanding microfinance assets followed the same trend, which might be ex-

plained by a cautious behavior by both MIV and MFI managers.

Indeed, in response to tightening liquidity measures due to the credit crisis following the

collapse of Leman Brothers, the aforementioned managers responded by reducing the size of

investments, shortening the tenor of loans and eventually postponing investment opportuni-

ties. Besides, the actual growth slowdown is viewed as a natural cycle in any rising industry,

and is used by many microfinance participants as an opportunity to consolidate and improve

portfolio quality and internal systems […] and is beneficial for maturing the industry in the long run

1 MicroRate surveys take into account Calvert Foundation, which is not considered in the present

thesis as a MIV. However, in order to keep consistency in the analysis, and considering minor reper-

cussions on the figures (i.e. Calvert microfinance portfolio doesn’t exceed US$ 45 million in 2008),

original data is kept without further adjustments.

2 A socially responsible investor takes into account social, ethical and environmental criteria alongside

conventional financial criteria in the investment decision making (adapted from Social Investment

Forum, 2003).

29

(MicroRate, 2009b, p.2). In fact, excessive growth rates from previous years are seen as finan-

cially unsustainable.

Figure 4.1 presents MIV’s total outstanding portfolio, microfinance portfolio and number of

MIVs surveyed from 2004 to 2008.

Figure 4.1: MIV asset growth 2005-2008

Source: MicroRate (2009c)

A typical characteristic of the industry is that assets are heavily concentrated amongst MIVs;

top 10 MIVs in 2008 account for 63% of the total microfinance assets. That remains consistent

with previous years, i.e., 65% in 2006 and 61% in 2007 (cf. Appendix III). Overall, the larger

part of portfolios of MIVs is held in hard currencies; 70% according to MicroRate (2009a) and

CGAP (2009b).

Furthermore, 82% of MIV’s microfinance assets are structured as debt instrument1 as of De-

cember, 2008. Funding with debt is a conservative approach that prevails predominately

more in LAC where MicroRate (2009a) notices that debt accounts for 91% of the participant

MIV‘s outstanding portfolio. Globally, comparing with past years, debt remains fixed as a

proportion of microfinance portfolio, whereas equity2 decreased from 16% in 2007 to 13% in

1 Debt funding: “The investor makes a loan to the MFI, and occasionally is legally subordinated to the

claims of other lenders/depositors, in which case it may function as quasi-equity for regulatory pur-

poses.” (CGAP, 2005, p.4).

2 Equity funding: “The investor buys stock in the MFI, becomes a voting shareholder, and often con-

trols a seat on the board of directors.” (CGAP, 2005, p.4).

30

2008. Overall, total MIV equity grew by 22% during 2008, but microfinance equity assets

only grew by 4%. Specifically, 10 MIVs saw a decline in microfinance equity assets by 33% in

2008 (MicroRate, 2009c, p.6).

More commercial MIVs tend to invest in debt, while equity investments are perceived rela-

tively more risky and exits from equity can prove to be difficult. Even if debt investments

from MIVs are becoming more competitive and the debt market may sooner or later satu-

rate, the equity market should become more transparent in order to offer real exit alternatives

(MicroRate, 2006, p.4), i.e., development of a secondary market in order to enhance liquidity

of equity shares.

According to Littlefield & al. (2007), the lack of exit alternatives for investors creates pres-

sure and leads to a market concentration; more specifically, most exits of equity investment

occur when existing shareholders or specialized microfinance investors take over the impli-

cated equity position.

On the other hand, exit alternatives have started to emerge in some markets. CGAP (2008b)

argues that Compartamos IPO1 is one of the first real exit opportunities that microfinance

has recorded, beyond sales to the aforementioned investors. Larger and more mature mar-

kets like India and Mexico might allow an eventual public offer attracting equity-based

MIVs. Moreover, IFIs could make a significant contribution in building the equity capital of

emerging MFIs by shifting funding, directly and indirectly, through MIVs and other MIIs

(Reille, 2007).

In addition to debt and equity, the remaining proportion is split between guarantees2 for

local investments, which are quasi-insignificant and on the decline (<1%), and “other micro-

finance assets” (5%). MIVs are intentionally maintaining a significant proportion of “other

microfinance assets”, “as a protection against any early redemption in addition to pending disburse-

ments, […] and as MIVs expect a further deterioration in their portfolio quality and are making ade-

quate loan loss provisions.” (MicroRate, 2009b, p.2). Paradoxically, some MIV managers depict

slower growth for microfinance as a break that “allows MFIs to strengthen their organization”

(MicroRate, 2009b, p. 36).

The actual situation contrasts with past economic downturns when microfinance showed an

immunity when facing macroeconomic and country risk crises, e.g. Bolivian, Peruvian and

Dominican cases in Calderón (2006). Indeed, microfinance is to a large extent more linked to

mainstream financial markets than it was during past financial crises. Considering higher

commercialization and exposure to currency volatility3, Latin American microfinance has

1 Cf. section 2.3.

2 Guarantee: an ex-pioneer microfinance instrument where “the investor guarantees MFI borrowings

from local banks or capital markets.” (CGAP, 2005, p.4).

3 e.g., 85% of IFIs loans to MFIs are financed in hard currency (CGAP, 2009a).

31

been particularly hit by the crisis (Dokulilova & al. 2009, p.18). Moreover, based on a sample

of eight Latin American countries1 for 2007 and 2008, an empirical study (MIX, 2010a) finds

that the correlation, between the portfolio growth of MFIs and the economic growth is be-

coming more procyclical than counter-cyclical (MIX, 2010a, p.3).

MIV managers notice deterioration in Latin American MFIs portfolio quality, although not

to severe extents (MicroRate, 2009a). Exceptions are Argentina2, Bolivia and particularly Ni-

caragua, where PaR303 has doubled during 2008 in several MFIs and liquidity has critically

plunged; cf. MicroRate (2009a) and CGAP (2010b). In the case of Nicaragua, a key factor driv-

ing microfinance risk management and leading to provision reserves, apart from the recent

global financial crisis, is the volatile political and economic situation (e.g., responsAbility,

2009). MIVs’ managers expect that current conditions will lead to an important consolidation

among MFIs in Nicaragua, “where arguably too many MFIs are vying for too few clients.” (Mi-

croRate, 2009a, p.36).

In this context IFIs might play a significant role in facilitating funding, particularly to lower-

tiers to MFIs that are more vulnerable. In December 2008, the IDB launched through the

Multilateral Investment Fund (MIF)4 an initiative to supply up to US$ 20 million in liquidity

for MFIs in LAC. Also, in February 2009, KfW and the IFC initiated a global Microfinance

Enhancement Facility (MEF)5 designed to supply liquidity of up to US$ 500 million in a

short-to-medium term period, to 200 MFIs facing funding deficit due to the financial crisis.

MEF planned to lend through leading MIVs6 in order to reach MFIs and not to crowd pri-

vate funding out (cf. sub-section 3.1.2). In reality, according to MicroRate (2009a):

� Several MFIs in LAC report that IFIs revoked negotiations.

� IFIs tend to invest predominantly in low risk positions and not play an active role in

countries with a high level of demand of capital, e.g., Argentina, Bolivia and Ecua-

dor.

� Lower-tier MFIs (more vulnerable and less developed) state that IFI liquidity supply

simply does not reach them.

1 i.e., Bolivia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru.

2 Argentine MIV exposure is insignificant, i.e., US$3.1 million as of September 2008 (MicroRate, 2009a).

3 i.e., Portfolio at Risk greater than 30 days = (Outstanding Balance on Arrears over 30 days + Total

Gross Outstanding Refinanced (restructured) Portfolio) / Total Outstanding Gross Portfolio. “Most

widely accepted measure of portfolio quality [...] PaR shows the portion of the portfolio that is conta-

minated by arrears and therefore at risk of not being repaid” (MicroRate & IDB, 2003, p.6).

4 The MIF is an autonomous fund administered by the IDB. It was established in 2004 as a lender-of-

last-resort for the Latin American microfinance industry (source: website of IDB:

http://www.iadb.org).

5 MEF is funded of US$ 150 million from IFC and US$ 130 million from KfW.

6 i.e., BlueOrchard, responsAbility and Cyrano Management.

32

To conclude, there is a need for IFIs to proactively manage flows of emergency funding in

countries where demand for microcredit critically outpace current funding (MicroRate,

2009a, p.37). Focusing predominantly “on creating and supporting commercially viable MFIs, i.e.,

tier 1 MFIs, (IFC, 2009, p.2), is not necessarily the right response by IFIs to the microfinance

community enduring the global recession. As a request for introspection, a bottom-up ap-

proach might be a more adequate solution during a financial downturn period.

33

5. Country risk: implications for microfinance

This chapter defines country risk and delimits the scope of study. Geographical asset allocations and

country exposures of microfinance investment vehicles are tackled in order to emphasize their country

concentration. A manager of microfinance investment vehicles - Global Partnerships- is introduced

and subsequently analyzed. Last section considers interest rate ceilings from a theoretical and practic-

al viewpoint.

5.1 Country risk as a broad concept

Considerable misinterpretation surrounds the concept of country risk (Nagy, 1979). “Coun-

try risk”, “sovereign risk” or “political risk” are often regarded as substitute terminologies;

in fact, these concepts are not similar. In a first step, “country risk” should be framed not to

create confusion.

When a MIV, a MII, or any cross-border investor start to expand their respective investment

positions worldwide, they have to deal with new environments, which are composed of

different risks and uncertainties. Various definitions and several terminologies exist to tackle

the risk related to a foreign investment. Subsequently, definitions of country risk are pre-

sented and interpreted within a MIV framework:

“Country risk may […] be defined as exposure to a loss in cross-border lending, caused by events in a

particular country, events which are, at least to some extent, under the control of the government but

definitely not under the control of a private enterprise or individual.”(Nagy, 1979, p.13).

First, country risk, as defined here, only concerns cross-border MFI loans; thus, the risk of

MFI loans funded in domestic currency is excluded. Country risk exposure should not be

determined by where MIVs are incorporated and traded, but from where they do business.

MIVs are typically incorporated in Western Europe and North America; 86% of MIV AUM is

domiciled in Western Europe and 8% in North America (CGAP, 2009e, p.2).

Second, all MIVs and other cross-border investors that are lending in a country - whether

through the government, a microfinance network, or directly to an MFI - are exposed to

country risk. Hence, country risk is a broader concept than “sovereign risk”, “which is con-

cerned with the state’s capacity to fulfill its obligations. It monitors the public finance sector as well

as some of the more qualitative aspects such as the fight against corruption or the degree of the admin-

istration’s independence vis-à-vis business and political groups.” (Bouchet & al. 2003, p.89).

Third, materialization of country risk is strictly related to events, at least to some extent, un-

der the control of the local government. Thus, an MFI that defaults on its debt caused by a

microfinance delinquency crisis is country risk if the default is “the result of mismanagement of

the economy by the government.” Conversely, it is commercial risk if it is the result of the misma-

nagement” of the MFI. (Nagy, 1979, p.13).

34

A border case is natural disasters. The recent earthquake in Haiti demonstrates the need to

be conscious of potential natural calamities that can adversely impact an MFI; in this case a

whole nation. CGAP reported two weeks after the earthquake in regards to a Port-au-Prince-

based MFI: “Sogesol to date has not found 50% of its clients, and estimates that 40% of its portfolio

will be lost.”1 Imaging a fictitious case where a MIV might have had invested in Sogesol, the

question is; should the MIV have had made allowance for a potential earthquake in its as-

sessment of country risk?

According to Bouchet & al. (2004, p.16), the answer is positive; “natural risks refer to the natu-

ral phenomena (seismicity, weather) that may negatively impact the business conditions. […] in order

to belong to the country risk category, the features of the events to be included must be different from

those at home.” On the other hand, this study follows Nagy (1979, p.13) in order to mitigate

the answer; if natural disasters are “unforeseeable, they cannot be considered as country risk. But

if past experience shows that they have a tendency to reoccur periodically – e.g., typhoons – then the

government can make certain preparations for such a contingency in order to minimize their harmful

effects.” In short, country risk refers solely to inefficiencies in regards with local institutions.

5.2 Geographical allocation and concentration risk

In order to recognize and evaluate any potential risks for MIVs, concentration can be consi-

dered as a main structural factor that increases the (joint) default probabilities (Gatzert et al.

2007). Thus, monitoring MIVs’ cross-border exposure is a key component of risk analysis,

especially when the context is related to developing economies. The purpose is not only to

closely monitor the confidence or nervousness of MIVs in a particular country, but also to

assess the likelihood and extent of spreadable effects within a region.

5.2.1 Global overview

When it comes to regional allocation, MIVs investments are heavily concentrated in the LAC

and ECA regions. Both markets combined represent 78% of the investments in microfinance

(MicroRate, 2009c, p.7). Latest CGAP and MicroRate surveys are relatively consistent with

regards to the geographical allocation of MIVs’ investments. Subsequently, one can assume

that relative country exposures would be equivalent for both surveyed samples. Looking at

the market concentration of exposures, approx. 60% of the total of MIV investments is found

in five countries (CGAP, 2009d). Astonishingly, MIVs focus, on average, 40% of their in-

vestments in solely five MFIs (CGAP, 2008b, p.12).

1 “The Haiti Earthquake: How microfinance is helping“ (Website of CGAP, section: “Media Center –

Features”, http://www.cgap.org (Accessed on March 15, 2010)

35

Figures 5.1 and 5.2 provide geographical allocation in terms of MIV microfinance assets and

MIV total assets, from MicroRate (2009c) and CGAP (2009e) survey results respectively.

Figure 5.1: Geographic concentration in 2008 (MicroRate) Figure 5.2: Geographic concentration in 2008 (CGAP)

Source: own illustration, based on MicroRate (2009c) and CGAP (2009e)

LAC has the highest number of MIVs investing in the region with 53 MIVs, which is fol-

lowed by ECA with investment from 43 different MIVs; SSA SA and EAP have respectively

33, 32 and 23. Lastly, MENA has only 11 MIVs investing in the region (MicroRate, 2009c,

p.9).

During 2008, the SSA market share decreased from 7% to 5%. SA observed a growth in mi-

crofinance assets of 67% from US$ 223 million in 2007 to US$ 373 million in 2008, mainly

attributed to new and increased investments by Oikocredit, BlueOrchard, responsAbility

and Triodos MIVs. EAP witnessed a growth of 30% from US$ 107 million in 2007 to US$ 139

million in 2008 (MicroRate, 2009c). MENA only has 1% of global microfinance investment,

but is the fastest growing region with a 552% increase in investment, principally attributed

to Oikocredit and Triodos Microfinance Fund (MicroRate, 2009c, p.8). Reflecting the instabili-

ty of flows of foreign capital particularly in Northern Africa (tackled in section 3.1.2), from

2006 to 2007, MENA lost almost half of its microfinance assets. The major contributor to this

sudden outflow is a concern over “local regulatory requirements regarding information disclo-

sure” (MicroRate, 2008, p.26).

ECA is outpacing LAC in terms of regional growth and market share. In regards to micro-

finance investments, LAC observed a slower MIV microfinance portfolio growth in compar-

ison to previous years, from US$ 1.21 billion in 2007 to US$ 1.28 billion 2008. During the

same period ECA saw its microfinance investments growing from US$ 1.13 billion to US$

1.58 billion. According to MicroRate (2009b, p.2,) the deceleration of growth in LAC is “partly

due to MIVs’ internal investment limits being reached in several Latin American countries.”

Indeed, there are too many MIVs and primary investors prospecting too few MFIs. This be-

havior leads to a bunching effect, i.e., risk concentration towards few large MFIs (CGAP,

35%

43%

10%

5%

4% 1% 2%

LAC

ECA

SA

SSA

EAP

MENA

Other

29%

47%

5%

6%

6%

1%

6%LAC

ECA

SA

SSA

EAP

MENA

Other

36

2008b, p.12). Reille (2007) observes that only 400-450 MFIs are considered to be investable as

of mid-2007, where, relying to MicroRate’s findings, the majority of them is situated in the

LAC and ECA regions.

5.2.2 Country exposure in Latin America and the Caribbean

Depicting the regional concentration of cross-border funding, microfinance development

vehicles, being the most socially focused MIVs, have traditionally allocated more than half

of their capital to LAC (CGAP, 2008b). Lately, this phenomenon of regional concentration

has changed to reveal a slightly global inverse trend1; however MIVs’ asset allocations in

LAC remain concentrated in some specific Latin American countries.

Table 5.1 illustrates the outstanding MIV assets by country and the share of each country; it

is based on a survey of 23 major MIVs investing in the LAC region as of September 2008.

Appendix IV lists the surveyed participants.

Table 5.1: Outstanding MIV assets in 2008

Country MIV Assets

(US$ million)

% of

Total

Peru $205.5 21.76%

Nicaragua $166.3 17.61%

Ecuador $144.6 15.31%

Mexico $137.4 14.55%

Bolivia $124.4 13.17%

Colombia $86.3 9.14%

El Salvador $40.6 4.30%

Honduras $13.9 1.47%

Paraguay $9.8 1.04%

Brazil $6.9 0.73%

Guatemala $3.7 0.39%

Argentina $3.1 0.33%

Panama $1.0 0.11%

Dominican Republic $0.6 0.06%

Haiti $0.2 0.02%

Venezuela $0.1 0.01%

Total $944.3 100.00%

Source: adapted from MicroRate (2009a)

None of the surveyed MIVs invests in Chile, Costa Rica, Uruguay or in the English-speaking

Caribbean; at least as of September 2008 (MicroRate, 2009a, p.33).

1 e.g., 40% of socially focused MIVs’ investments are allocated in LAC as of December 2008 (CGAP,

2009d, p.23).

37

Peruvian MFIs hold more funds from MIVs than any other country in LAC. Not surprisingly

since Peru has the largest microfinance sector in the region, excellent economic growth rates,

and stable socio-political situation. According to MicroRate (2009a), the high degree of com-

petition among Peruvian MFIs has enhanced product innovations, pushed MFIs in new

geographic regions (from urban to rural), and strengthened organizational structures and

risk management. Overall, portfolio quality of Peruvian MFIs follows a favorable trend, e.g.,

PaR30 from 3.6% in 2007 to 3.2% in 2008, contrasting with LAC (from 3% in 2007 to 4% in

2008) (MIX, 2010c, p.8). The mature regulatory framework for MFIs plays a preponderant

role in Peruvian microfinance success and confidence; MFIs follow from July 2009 Basel II

norms1 in order to regulate capital requirements, particularly to cover credit risk and adopt

contingency measures that include consequent provisions due to the global crisis. However,

several MIVs and IFIs report “reaching their lending limits for the country.” (MicroRate, 2009a,

p.29). Thus, not approved deposit-taking MFIs may face difficulties for raising capital in the

near future.

Nicaragua and Ecuador are both ranked ahead of Bolivia, Mexico and Colombia. The high

concentration of MIV investments in these two countries is unexpected and bearing concerns

because of their low country creditworthiness, due to political and economic instability. The

increasing inflation is a persistent concern that leaves no leeway for MFIs to earn positive

returns in real terms. Moreover, Nicaraguan and Ecuadorian microfinance sectors face a risk

of multiple borrowings by over-indebted clients, due to a high density of MFIs, microfinance

market saturation and unhealthy competition among them (MicroRate , 2009a).

Regarding overall MIVs’ country concentration, it has to be pointed out that seven countries

hold 96% of the total of MIVs’ investment in LAC; namely Peru, Nicaragua, Ecuador, Mex-

ico, Bolivia and Colombia.

For a more precise evaluation of concentration of country exposure, an indicator is devel-

oped using the conceptual framework of the Herfindahl-Hirschman Index (HHI)2. The LAC

concentration index is defined as follows:

�� = ��

�

�� , (5.1)

Where S� denotes share of outstanding MIVs’ assets of country �, i.e., country exposure in %,

last column of Table 5.1.

1 i.e., the international standards for banking system regulation (cf. website of the Bank for Interna-

tional Settlements (BIS), http://www.bis.org (Accessed on March 15, 2010).

2 HHI is a widely accepted measure of industry concentration, cf. Hirschman (1964).

38

Where � is number of countries having a microfinance sector in LAC; the MIX lists exhaus-

tively 19 countries for region.

Equation 5.1 ranges from 1/N to 1; the higher is the index value, the higher is the concentra-

tion.

For instance, if MIVs were funding exclusively one country, equation 5.1 would be equal to

one. Besides, it should be emphasized that the index measures concentration, and thus can-

not be extrapolated as a proxy for competition (Podpiera & Cihák, 2005), because competi-

tion variables are strictly exogenous to such an index (Schwaiger & Liebeg, 2009, referring

Wooldridge, 2002). The present issue at hand is first, to compare the index with an existing

scale, and second, to use the index as a benchmark for MIV Latin American investment con-

centration in sub-section 5.2.3.

For a matter of further comparability, the normalized concentration index is considered, and

computed as follows:

��∗ = �� − �

�� − �

� , (5.2)

Equation 5.2 ranges from 0 to 1; i.e., perfect granularity to total concentration (Schwaiger & Lie-

beg, 2009).

A comparative scale for normalized values, commonly used in the microfinance industry, is

as follows:

� <0.01 is considered a highly diversified portfolio in regards to country exposure.

� 0.01 – 0.1 is diversified.

� 0.1 – 0.15 is moderately concentrated.

� 0.15 – 0.2 is concentrated.

� >0.2 is highly concentrated.

Equation 5.2, i.e., the normalized LAC Concentration Index, resulting from last column in

Table 5.1 input, gives: ��∗ = �. �� (i.e., 10.38%), indicative of a moderate concen-

trated sample.

The outcome from the calculation of equation 4.2 enables to answer to which extent MIV

investments are concentrated amongst countries in LAC. Also, by utilizing the same MIV

sample from Table 5.1, a further analysis is conducted.

39

Table 5.2 provides a breakdown by country of MIV and MFI measures, by combining two

data sources, as of September and December 2008 respectively.

Table 5.2: MIV country exposure analysis

Country A. MIV

Assets

B. % of

Total A.

C. MFI

Assets

D. Nb.

of MFIs

E. % of

Total C.

F. Market

Penetration

G. Invest-

ment Ratio

Peru $205.5 21.76% $4,800.0 64 26.08% 4.28% -16.56%

Nicaragua $166.3 17.61% $676.6 30 3.68% 24.58% 379.04%

Ecuador $144.6 15.31% $1,500.0 52 8.15% 9.64% 87.88%

Mexico $137.4 14.55% $2,900.0 48 15.76% 4.74% -7.66%

Bolivia $124.4 13.17% $2,000.0 26 10.87% 6.22% 21.23%

Colombia $86.3 9.14% $4,000.0 21 21.73% 2.16% -57.95%

El Salvador $40.6 4.30% $467.2 18 2.54% 8.69% 69.37%

Honduras $13.9 1.47% $214.3 17 1.16% 6.49% 26.42%

Paraguay $9.8 1.04% $448.8 7 2.44% 2.18% -57.44%

Brazil $6.9 0.73% $677.5 37 3.68% 1.02% -80.15%

Guatemala $3.7 0.39% $184.7 19 1.00% 2.00% -60.96%

Argentina $3.1 0.33% $23.1 13 0.13% 13.42% 161.56%

Panama $1.0 0.11% $19.7 3 0.11% 5.08% -1.07%

Dominican $0.6 0.06% $233.5 7 1.27% 0.26% -94.99%

Haiti $0.2 0.02% $85.0 8 0.46% 0.24% -95.41%

Venezuela $0.1 0.01% $174.1 2 0.95% 0.06% -98.88%

Total $944.3 100% $18,404.5 372 100% 5.13% 0.00%

Source: own research, MicroRate (2009a) and themix.org

Columns A. and B. are identical to Table 5.1 and are incorporated in Table 5.2 for a matter of

clarity.

Column C. represents aggregated MFI assets by country, i.e., proxy for market size. Data is

gathered from the MIX Market1 and is self-reported by MFIs. In general, datasets from the

MIX don’t’ include all MFIs of the microfinance universe. However, a large fraction is

served, thus it allows to perform a comparative study amongst countries.

Column D. is the number of MFIs that the sample comprises. It is provided for a matter of

consistency; no calculation is based on this data column. Data comes from the MIX as well.

Column E. represents the share of MFI assets of each country, e.g. for Peru= 4800/18,000=

26.08%. Hence, Peruvian MFIs possess more than a quarter of the total assets in LAC coun-

1 Website of the MIX Market, section: “Microfinance Institutions – Country – Total Assets”,

http://www.mixmarket.org (Accessed on March 15, 2010).

40

tries, which have MIV investments and are represented in the present sample. Moreover,

five countries hold 83% of MFI assets, namely Peru, Ecuador, Mexico, Bolivia and Colombia.

Column F. provides a proxy of MIV market penetration; it is calculated by dividing column

A. by column C., i.e., MIV aggregated assets in country i divided by MFI aggregated assets

in country i. For instance, if in an (illusionary) country all MFIs were funded solely by MIV

investments, and not by IFIs, or by deposits etc., the market penetration ratio would equal

100%.

Particularly, two outliers stand out, Nicaragua and Argentina. For the former, the result is

again unexpected considering its limited market; MIVs that are funding Nicaraguan MFIs

have to face not only the aforementioned political and economic concerns, but also market

saturation. According to a study of the Economist Intelligence Unit (2007, p.9) on the micro-

finance business environment in Latin America, “Nicaragua is an “overperformer”, where “the

depth of its microfinance sector does not match its breadth.”

Argentina’s result might come as a surprise as well; the Economist Intelligence Unit (2007,

p.7) ranks it last among the countries in LAC, and in its study points out a mediocre micro-

finance regulatory framework and investment climate among the main reasons.

Column G. provides the “investment ratio” that is calculated as follows:

(� !"#$ % – � !"#$ ')) ⁄ (� !"#$ '). Intuitively, this ratio measures the relative discre-

pancy between how the MIV LAC market is shared, and how the MIV LAC market should

be shared assuming uniform market participants and no exogenous factors. In this analysis,

it’s an indicator exclusively for 2008. The “investment ratio” tries to distinguish which coun-

tries were falling out of favor of MIVs’ managers in 2008, and the other way around, which

countries are observing “over-attention” (cf. MicroRate, 2009a). As mentioned above, if mar-

ket participants – i.e., MFIs and MIVs- are uniform and no exogenous interference comes

into play, the ratio should be equal to 100% for country i, thus MIV country exposure should

perfectly reflect MFI assets distributed (by country).

One critical outlier value observed for Nicaragua is an over-exposure of MIV investments

compared to other countries in LAC. This result potentially means that MIVs had not al-

ready taken into account Nicaraguan instability in their decision-making for geographical

asset allocation in 2008. Conversely, several MIVs reported to be “eager to reduce their expo-

sure in Nicaragua” (MicroRate, 2009a, p.34).

The above analysis tries to capture the differences between countries, e.g., a standalone val-

ue is not of a great pertinence. Second, a perplexity may arise if you take into account MFI

data and representative proportions, e.g., if 80% of Peruvian MFIs are included, are 80%

Nicaraguan MFIs as well?

In this analysis however, the question is different: Is the dataset representative and propor-

tionate with regards to investable MFIs that MIVs might prospect? The answer is a plausible

“yes”. Following Galema & al. (2009), MFIs are self-reporting on a voluntary base in the

41

MIX, but data entry is closely monitored by the latter. MFIs have to enclose documentation

that supports the data, e.g., audited financial statements and annual reports. Therefore, the

dataset gathered from the MIX fits assumingly well the purpose of the present analysis.

Further relevant analysis would be possible if a country breakdown by MIV was published –

i.e., country exposure of each MIV in the sample. Such a data would allow to perform vari-

ous empirical studies in order, for instance, to weight the risk and return of an MIV consi-

dering country exposures. Besides MicroRate’s LAC sample for 2008, no other available data

exists for MIVs to reveal trends across the years; the pertinence of such analysis might allow

observing dynamics of MIV investment strategies across countries. Considering that MIV

managers are in general not rebalancing their microfinance portfolios in an excessive way,

the result would be a linear trend for countries where MIV market penetration is high, and

outlier values might come from countries where MIVs are starting to pay attention. Relevant

questions that may arise can be: why are MIV managers increasing/decreasing their expo-

sure in a country? Can it be attributed to factors inherent in MFIs or to macroeconomic con-

ditions?

The empirical study that follows in chapter 6 aims to answer the above questions by assess-

ing factors that a priori have an impact on the underlying asset quality of MIVs investing in

LAC. For this purpose, the next section presents an organization –Global Partnerships-

committed to Latin American microfinance that currently manages three MIVs investing

collectively in 28 MFIs across seven countries.

5.3 Global Partnerships Microfinance Funds

This section tackles Global Partnerships’ MIVs with a focus on their underlying portfolios

and country exposures. The present case study has been chosen for several reasons:

1. Global Partnerships is a philanthropic organization that possesses an important exper-

tise in Latin American microfinance,

2. its mission is undoubtedly socially-oriented,

3. its investments are solely concentrated in LAC, thus a comparison with LAC bench-

mark is feasible, and

4. the organization discloses essential information that permits analyses and empirical

research.

Moreover, through the investment vehicles it manages, Global Partnerships funds MFIs in

countries where political interferences are influencing microfinance and interest rate ceilings

subsist. In fact, Global Partnerships invest in seven countries, where two of them enforce

interest rate ceilings; namely Nicaragua and Ecuador.

42

5.3.1 Overview of Global Partnerships’ MIVs

Global Partnerships (GP) is a not-for-profit US-based organization founded in 1994 that

seeks to “provide leadership in the fight against global poverty” (GP, 2009a, p.8) by funding MFIs

through affordable loans for maximizing social impact. In short, GP is a socially-motivated

institutional investor. Moreover, GP provides MFIs with training to financial and manage-

ment technologies, e.g., foreign currency hedging techniques (IDB, 2010). GP has over US$

45 million in asset under management, an operating budget of US$ 3 million and has offices

in Seattle, Washington and Managua, Nicaragua.

Through its subsidiary GP Fund Management, a limited liability company based in Dela-

ware, GP manages three MIVs: Global Partnerships Microfinance Fund 2005, Global Part-

nerships Microfinance Fund 2006 and Global Partnerships Microfinance Fund 2008 (collec-

tively “GP MIVs”). 1

� Global Partnerships Microfinance Fund 2005 is a US$ 2.0 million assets MIV com-

posed of US$ 200,000 in philanthropic capital as equity leveraged 10 times by US$ 1.8

million in private capital from qualified individual and institutional investors. The

MIV is investing in eight MFIs in four Latin American countries.

� Global Partnerships Microfinance Fund 2006 is an US$ 8.5 million assets MIV com-

posed of US$ 255,000 in philanthropic capital as equity leveraged at a ratio of 32 to 1 by

US$ 8.245 million in socially motivated debt capital from qualified individual and in-

stitutional investors. The MIV closed in March 2007 and its capital was disbursed to

14 MFIs across six countries.

� Global Partnerships Microfinance Fund 2008 is a US$ 20 million assets MIV com-

posed of US$ 1.5 million in philanthropic capital as equity leveraged by $18.5 million in

socially motivated investment capital from qualified individual and institutional in-

vestors. The fund closed in November 2008 and was aiming to reach 30 MFIs in eight

countries.

GP MIVs are targeting MFIs that are “financially sound, competitively strong and displaying an

exceptional level of social impact.” (IDB, 2010, p.1). Thus, according to the categorization by

investment purpose (cf. section 4.1), GP MIVs are quasi-commercial ones, i.e., while social

aspects are key in regards to their performance, financial returns are considered as well.

By legal structure GP MIVs are categorized as collateralized obligations, more specifically

Collateralized Loan Obligations (CLOs); a securitization of a pool of loans that are made to

MFIs.

1 The description of MIVs that follows is adapted from the website of Global Partnerships, section:

“Funding Sources”, http://www.globalpartnerships.org (Accessed on March 15, 2010), GP (2009a) and

GP (2009b).

43

5.3.2 Country exposure and concentration

Besides funding MFIs through GP MIVs, the organization invests in Latin American micro-

finance through Developing World Markets Microfinance Fund1. The composition of the GP

MIVs’ portfolio is generally limited to loans to MFIs. Table 5.3 provides the entire invest-

ment positions of GP aggregated by MFI as of September 2009. Data is gathered from GP’s

website.

Table 5.3: Outstanding GP’s microfinance portfolio

MFI Country Outstanding

Loan

% of

Total

Country

exposure

CRECER Bolivia $2,850,000 7.71%

FUBODE Bolivia $900,000 2.43%

Pro Mujer in Bolivia Bolivia $2,750,000 7.44% 17.58%

D-MIRO Ecuador $1,000,000 2.70%

Espoir Ecuador $800,000 2.16%

FINCA Ecuador $1,000,000 2.70%

FODEMI Ecuador $1,000,000 2.70% 10.28%

ACCOVI El Salvador $4,550,000 12.30%

AMC de RL El Salvador $1,500,000 4.06%

Apoyo Integral El Salvador $5,200,000 14.06%

Enlace El Salvador $632,500 1.71% 32.13%

Adelante Honduras $150,000 0.41%

FAMA Honduras $300,000 0.81%

FUNDAHMICRO Honduras $250,000 0.68% 1.89%

FRAC Mexico $500,000 1.35% 1.35%

ACODEP Nicaragua $750,000 2.03%

Coop 20 de Abril Nicaragua $500,000 1.35%

FDL Nicaragua $2,000,000 5.41%

FUNDENUSE Nicaragua $2,000,000 5.41%

F.J. Nieborowski Nicaragua $2,000,000 5.41%

León 2000 Nicaragua $300,000 0.81%

PRODESA Nicaragua $1,800,000 4.87%

Pro Mujer Nicaragua Nicaragua $250,000 0.68% 25.96%

Arariwa Peru $500,000 1.35%

Caja Nuestra Gente Peru $750,000 2.03%

CREDIVISION Peru $1,250,000 3.38%

FONDESURCO Peru $750,000 2.03%

PRISM Peru $750,000 2.03% 10.82%

TOTAL

$36,982,500 100.00%

Source: own research, globalpartnerships.org

1 Developing World Markets has about US$ 600 million of microfinance AUM, and has made invest-

ments in over 100 MFIs worldwide. Website of Developing World Markets, section: “Investment

Management - AUM”, http://www.dwmarkets.com (Accessed on March 15, 2010).

44

GP’s portfolio consists of approx. US$ 37 million in MFI loans, ranging in size from US$

150,000 to US$ 5,200,000, to 28 participating MFIs in seven countries in Central and South

America. GP’s investments are quite concentrated from an individual MFIs context. The

organization’s five largest microfinance investments account for almost half of the portfolio

(i.e., 47%). From a country exposure perspective, 96% of GP’s investments are concentrated

in solely 5 countries; namely Bolivia, Ecuador, El Salvador, Nicaragua and Peru.

Applying identical framework as Equation 5.2, i.e., the normalized LAC Concentration In-

dex, the normalized GP concentration index is computed as follows:

��*+∗ = ��*+ − ��

� − ��

, (5.3)

Where � is equal to 19, being the number of countries having a microfinance sector in LAC

(cf. section 5.2). In order to remain consistent, GP’s scope of investment cannot shift outside

LAC; hence, its investment perspectives are exclusively limited to the Latin American mar-

ket1.

Input from the last column in Table 5.3 is applied to equation 5.3, and results:

��*+∗ = �. ��, , which is indicative of a concentrated sample, not far to be highly con-

centrated (at 0.2, cf. section 5.2 for benchmark scale).

An interesting comparison can be drawn with the result from the sample of MIVs investing

in LAC (cf. equation 5.2, ��∗ = 0.1038); GP’s investment positions are almost two

times more concentrated than the MIV industry benchmark. This highlights high country

exposures and concerns. A quarter of the portfolio is allocated to MFIs in Nicaragua, which

as mentioned before endures severe political instability. However, GP’s regional due dili-

gence is relatively high; it possesses an office on the spot and a long record of expertise with

regards to local conditions.

“When expanding geographic scope, it is critical to be on the ground in the new markets, understand-

ing the same realities, and experiencing the same strengths and weaknesses.” (Unitus Capital MIV

survey, 2009, p.12, about follow-on MIVs). The advantage is that MIVs focusing on a single

region might have superior deal flow; the drawback is a limited portfolio diversification.

GP’s MIVs may typically attract “fund of funds” that don’t have necessary local industry

expertise and seek to maximize diversification; e.g. GP’s MIVs’ underlying portfolio would

not “overlap” with a MIV investing strictly outside LAC.

1 Mission statement: “GP expands opportunity for people living in poverty by supporting microfinance and

other sustainable solutions in Latin America […] the purpose [of GP] is to promote the efforts of microfinance

in Latin America.” Website of GP, section: “About us – Mission”, http://www.globalpartnerships.org

(Accessed on March 15, 2010).

45

5.3.3 Capital structure and MFI debt default

GP is issuing microfinance CLOs whose payments come from a pool of loans made to MFIs.

These loans have to be legally distinguished from other obligations of GP. For this purpose,

GP has created the independent legal entity GP Fund Management, as a special purpose

vehicle (SPV). SPVs are commonly used to securitize loans. GP Fund Management is hold-

ing the underlying portfolios of loans and GP works as the asset manager, e.g., selecting the

MFIs underlying the portfolio in the CLOs.

In general, a CLO gains exposure thanks or due to the credit of a portfolio of assets (pooling

the assets), and divides that credit risk among different notes (tranching the assets). It sells

rights to the cash flows and risk associated with the pool of assets. The loss of an investor’s

principal (e.g., of the loan) is applied in reverse order of seniority. Senior tranches that are

less return-rewarded have priority over subordinated tranches, which have priority over the

equity. CLO equity holders are non-recourse investors and are the last to be paid (e.g.,

Deng, Gabriel and Sanders 2008).

Table 5.4 provides the capital structure of Global Partnerships Microfinance Fund 2005,

where investors receive quarterly interests with a spread above treasuries depending on the

seniority of the note.

Table 5.4: Capital structure

Tranche Size (% of

total fund)

Size

(US$) Protection

Spread above base rate (4.5

yr US Treasury)

Senior 80% $1,600,000 Third Loss 150 b.p.

Subordinated 10% $200,000 Second Loss 250 b.p.

Equity & Cash

(Philantropic

Capital)

10% $200,000 First Loss

Initial equity provided by GP

in the form of contributed

loans to MFIs plus projected

build up of Cash Reserve

Source: adapted from GP (2005)

Collateralized obligations are becoming popular in the microfinance industry, as this struc-

ture of offering senior tranches attracts risk-averse investors and investment institutions that

are only allowed to invest in investment-grade products. (e.g., Dieckmann, 2007). In addi-

tion, securitization in microfinance by minimizing adverse selection may help to attract in-

vestors not familiar with microfinance. According to Gorton and Souleles (2005, p. 14), “pool-

ing minimizes the potential adverse selection problem associated with the selection of the assets to be

sold to the SPV.” Depending on the MFIs selected, tranching divides the risk of loss - due to

the default of an MFI - based on seniority; from the equity to the senior tranche, known as

46

the “waterfall structure.” Since tranching is based on preeminence of tranches, “the risk of

loss due to default of the underlying assets is stratified.”

On the other hand, potentials investors have to be aware of a main setback in regards to de-

fault correlation among tranches; the MFI cross-default risk. According to a recent MIV sur-

vey (Abrams, 2009, p.12), “MIV loan documentation often specifies that an MFI default to one

lender will cause cross-default to all of its other funders”. One needs to take into account to which

extent defaults by different micro-borrowers are likely to gather, as cross-default would im-

pact severely its funding liabilities to MIVs. In fact, “an MFI that concentrates its operations in a

municipality or small region not only has difficulty achieving high levels of efficiency, but also engag-

es in a dangerous concentration of geographic and sector risk.” (Berger & al., 2006, p.114)

Rephrasing Mackenzie’s (2008, p.24) dissertation about the credit crisis, “some defaults might

be the result of idiosyncratic problems causing the bankruptcy of a single [MFI], but others reflect

systemic factors such as poor conditions in the economy as a whole.” If the latter happens, then one

default of MFI might be accompanied by others. Default correlation should not be underes-

timated considering the fact that the Latin American microfinance industry is highly concen-

trated from a micro-borrower and an MFI feature.

Having pointed out the default correlation issue, an elementary interrogation arises; what is

the microfinance debt default rate? Before singing the praises of MIVs, one should be aware

of the relative lack of global studies and surveys performed in regards to microfinance de-

fault rates, explicitly MFIs’ debt default to MIVs.

Disclosure, reliability of data, and longer track records limit possibilities of empirical re-

search in microfinance. To date, one MIV survey tackled this concern. Abrams’ (2009) survey

includes 44 MIVs covering a period from 1994 through mid-2008. Abrams (2009, p.8) finds:

� 2% default rate based on number of instances of default as a percentage of cumula-

tive disbursed MIV loans (60 MIV loans to MFI defaulted out of 3000 sampled).

� 0.2% default rate on a volume basis ($US 8.1 million out of $US 4.1 billion)

The aforementioned results are a baseline; the survey focuses strictly on MIV loans, a large

number of defaults are not disclosed, and “default” definition varies across surveyed partic-

ipants (Abrams, 2009).

Numerous causes and factors drive MFI risk of default, being potential threats for investors.

Some are endogenous to MFIs, e.g., poor portfolio quality, illiquidity, fraud within MFIs

(Abrams, 2009, p.9); and others are exogenous and may impact significantly worthiness of

the MIV industry. The economic crisis has transformed perceptions of the microfinance area.

Banana Skins survey (CSFI, 2009) lists major risks perceived by different microfinance par-

47

ticipants, i.e., practitioners, investors, regulators and deposit-takers1. Risks considered as

minor in 2008 are now perceived as potential threats, e.g., business environment and regula-

tory framework. Table 5.5 lists biggest threats from the viewpoint of investors.

Table 5.5: Investor’s Banana Skins

Biggest risks Fastest risers

1 Refinancing 1 Credit risk

2 Foreign currency 2 Macro-economic trends

3 Credit risk 3 Political interference

4 Macro-economic trends 4 Liquidity

5 Liquidity 5 Foreign currency

6 Corporate governance 6 Refinancing

7 Management quality 7 Profitability

8 Too little funding 8 Too little funding

9 Inappropriate regulation 9 Competition

10 Political interference 10 Interest rates

Source: CSFI (2009)

.

Economic issues are concerns across all respondents. Investors adversely perceive aspects of

the global crisis that might reduce the value of their commitments: liquidity and funding

management of MFIs, domestic currency volatility, and the impact of credit risk regarding

MIVs’ soundness and profitability. Quality of management and corporate governance in MFIs

are key factors and significant drivers in an investor’s decision-making. Moreover, on the

forefront of investors’ concerns is “the impact of regulation and political interference which may

increase due to the economic crisis.” (CSFI, 2009, p.9).

Political interference in microfinance takes many forms; e.g., loan forgiveness, subsidized

competition and interest rate ceilings (CSFI, 2009, p.25). The latter is tackled in next section

from a theoretical viewpoint. The empirical study following in chapter 6 aims to assess,

among other country-specific factors, the impact of interest rate ceilings on MFI portfolio

quality underlying GP MIVs.

1 Respectively: “people who run or work in MFIs”, “people who invest in MFIs”, “government offi-

cials and those who regulate MFIs”, and “respondents from MFIs which take savers’ deposits. (CSFI,

2009, p.9-10).

48

5.3 Interest rate ceilings

Due to its historically social and developmental role, “microfinance has been prone to political

interference in certain countries.” (Fitch Ratings, 2008, p.18). Populism is often the trigger of

inadequate policies and government decisions that adversely impact microfinance. Accord-

ing to Fitch (2008), the most damaging political interferences in microfinance are interest rate

ceilings, subsidized financing at below market rates, and the support of the government to

micro-borrowers not to repay loans.

This section analyzes the goals and impact of government imposed interest rate ceilings on

MFIs. While the goal is to prevent usurious rates of interest, it ends up hurting the poor, as

microfinance requires high interest rates (in comparison with commercial banks) when con-

sidering additional risk factors and costs.

5.3.1 Why are interest rates so high in microfinance?

Interest rate is a controversial topic in microfinance. At first glance, interest rates charged by

some MFIs seem to be a usurious burden on the micro-entrepreneur poor. For example, in

Asian MFIs, interest rates range from 30% to 70% annually (on a reducing balance basis) in

addition to other commissions and fees. Some MFIs also have compulsory savings compo-

nents which increase the effective interest rates for micro-borrowers that are required to pay

during each repayment cycle.

The “interest rates” issue have attracted significant attention from political leaders in Latin

America (Nicaragua), in Africa (Ethiopia) (CGAP, 2004a), and particularly in Asia (Bangla-

desh, Cambodia, India, Pakistan, and Sri Lanka) (Fernando, 2006).

Interest rates charged by MFIs cannot be compared to commercial banks, or heavily subsi-

dized not-for-profit programs as their cost structures and funding expenses differ consider-

ably. MFIs seeking sustainability without donor or government dependency must charge

interest rates that cover their costs and enable them to be self-sustaining in the future (Fer-

nando 2006).

To determine why interest rates ceilings may not be enforce, it is necessary to understand

the factors that can cause high interest rates in microfinance; a key issue considering the em-

pirical study following in chapter 6, because interest rates charged by MFIs are a major

component of their portfolio quality:

� Operational Expense: Economies of scale is a major issue; microloans are propor-

tionally more expensive to administer than commercial loans. Microloans are very

small, provided in areas with a low population density, and involve more client

evaluation to counteract the lack of reliable data on a potential micro-borrower’s

business and credit history. As tackled in chapter 2, typically MFI borrowers provide

49

no collateral; have no credit history records or financial statements for their micro-

enterprise. Due to that, micro-borrowers require more intensive monitoring than

commercial lending (Rosenberg, 2006).

� Cost of Funds: The level of interest rate an MFI is charging must take into considera-

tion access to additional capital for its loan portfolio. As the microloans lent by MFIs

are largely unsecured, they may be charge higher interest rates to cover the default

risk of micro-borrowers. (Fernando 2006).

� Loan Loss Provisions: MFIs must provision of delinquent loans. Given the lack of

collateral in microfinance, provisioning is necessary for MFIs. While operational ex-

pense, cost of funds, and loan loss provisions are present in all types of lending (cf.

chapter 2), only cost of capital and provisions are proportional to the loan size. (Ro-

senberg Presentation, 2006).

� Inflation: MFIs must also account for inflation in their lending. “Inflation adds to the

cost of microfinance funds by eroding microlenders' equity. Thus, higher inflation rates con-

tribute to higher nominal microcredit interest rates through their effect on the real value of

equity.” (Fernando 2006, p.2).

� Profits: MFIs that seek to be sustainable outside of subsidies must charge, by defini-

tion following chapter 2, enough to have a margin or profit level that allows them to

grow their portfolio or attract additional external funding.

While an argument could have been that an MFI inefficiency drives up the interest rates

charged, in fact even the most efficient MFIs charge more than commercial banks (Rosen-

berg, 2006). MFIs that are more efficient might indeed be able to provide microfinance ser-

vices at lower expenses than compared to inefficient MFIs in the same context. A competi-

tive environment might reduce the interest rates charged.

5.4.2 Interest rate ceilings can damage microfinance and affect the poor

From a microeconomic viewpoint there are several levels where information asymmetry can

impede microfinance developmental effectiveness and efficiency. Interest rate ceilings are

essentially price controls on microcredit which can in fact harm those they intend to help

and lead to overall welfare loss.

50

Consider the following reflections:

� “Ceilings on interest rates prevented financial institutions from significantly expanding out-

reach to poor households. As transaction costs tend to be constant per loan independent of

loan size, interest rate ceilings and credit subsidies led to concentrations in the loan portfolio,

allocating relatively large loans to a few big farmers, neglecting the small and the poor. Banks

shifted transaction costs to borrowers, including legal and illegal charges, making cheap cre-

dit expensive to the end user.” (Quinones & Seibel, 2000, p.196).

� “Because small-scale loans are more expensive than large loans, rate ceilings could encourage

microcredit lenders to desert poorer, small-scale loan clients. Rate ceilings would change the

nature of MFI lending, creating a shift to more short-term loans. As a rate ceiling would in-

crease policy risk, and if inflation were expected to rise, longer-term loans would carry greater

risks. Rate ceilings would create an artificially high demand for microcredit relative to supply

and encourage credit officers and others to adopt rationing devices that, in turn, create rent-

seeking opportunities. (Fernando 2006, p.5)

From a socially-motivated investor viewpoint, two central issues emerge; mission drift and

portfolio quality deterioration.

Other harmful side effects of interest rate ceilings are not as apparent. Interest rate ceilings

can also lead to less transparency between the MFI and the micro-borrowers as it can en-

courage additional fees and charges that may not be clear to the client in order to provide

these services profitably (Rosenberg, 2006). Interest rate ceilings can also negatively affect

MFIs’ ability to fund their loan portfolio. Price ceilings will reduce and MFI’s portfolio

yield, narrowing their margin to levels where investment may be deterred. (Fernando, 2006).

CGAP (2004a) shows a significant correlation between interest rate ceilings and low levels of

market penetration among poor populations (i.e. living on less than US$2 per day). Poorer

households are more likely to be affected by interest rate ceilings as MFIs are prevented

from charging enough interest to cover the additional risk and expense of lending to low-

income households compared to higher income (Holmes, 2005).

Figure 5.3 shows the level of market penetration in countries with and without interest rate

ceilings. It is based on 23 countries with interest rate ceilings and 7 countries without ceil-

ings as of 2004.

51

Figure 5.3: Market penetration and interest rate ceilings


Governments must focus on ways to decrease the cost of microcredit without harming MFIs

or micro-borrowers. A recurrent proposed government action proposed by practitioners is

to create credit bureaus1 in order to reduce information asymmetry, thereby decreasing the

risk of lending and the expense of client research, and improving efficiency and lower the

related expenses of administering microloans. This might lower barriers to entry in provid-

ing microcredit, which can encourage competition further decreasing interest rates (Holmes,

2005).

Next chapter enhances an empirical model that aims to capture, among other country-

specific factors, the impact of an interest rate ceiling policy on MFI portfolio quality underly-

ing GP MIVs.

1 “A private credit bureau is defined as a private firm or nonprofit organization that maintains a database on the

creditworthiness of borrowers (persons or businesses) in the financial system and facilitates the exchange of

credit information among banks and financial institutions.” Website of Doing Business (IFC),

http://www.doingbusiness.org (Accessed on March 15, 2010).

52

6. Country risk and microfinance: a regression approach

Section 6.1 reviews the empirical literature dealing with the impact of country risk on portfolio quali-

ty in microfinance and introduces the empirical strategy. Section 6.2 describes the variables used in

this research. Finally, the results are presented and a conclusion is given in section 6.3.

6.1 The research model and preliminary analysis

6.1.1 Impact of country risk on portfolio quality in microfinance: a literature survey

Apart from microfinance-related empirical studies, “banking crises in emerging markets tend to

occur in response to a conjuncture of unfavorable developments in domestic and international mar-

kets.” (Eichengreen & Rose, 1997, p.28).

In microfinance, however, empirical studies show that MFIs1 and MIVs2 are rather resilient

to macroeconomic shocks. For example, Krauss & Walter (2008) find that in terms of global

market risk, MFIs are not correlated with capital markets. Janda & Svarovska (2009) demon-

strate that MIVs focusing on debt instruments represent an attractive opportunity for cross-

border investors regarding their portfolio diversification. However, resilience of microfin-

ance to domestic economic conditions is not confirmed; neither by Kraus & Walter (2008),

nor by Janda & Svarovska (2009). Krauss & Walter (2008, p.31) demonstrate a negative sig-

nificant impact of GDP on PaR-30 (the proxy for portfolio quality the study treats). From the

viewpoint of practitioners, PaR-30 is “the most widely accepted measure” of MFI portfolio quali-

ty (MicroRate & IADB, 2003, p.6). In a previous study, Krauss & Walter (2006) also show a

significant relation between growth and PaR-30. This indicator might reflect objectively the

complete institutional risk, thus making it an adequate proxy for MFI risk of default (Micro-

Rate & IADB, 2003).

Using panel regressions, Ahlin & Lin (2006) also tackle the question of MFI resilience to do-

mestic macroeconomic shocks. Among other MFI performance indicators, they regress on

the Write-off Ratio and PaR-30 in regards to assess MFI portfolio quality. Ahlin & Lin (2006,

p.6) show that “MFI performance seems to be non-negligibly driven by the macroeconomic envi-

ronment”, but find that only growth negatively affects both indicators (i.e., positive impact

with regards to the quality of a portfolio). Other macroeconomic variables3 have no statistic-

al significance except for inflation in few cases with low significances.

Gonzalez (2007) demonstrates a high resilience of MFI portfolios to economic shocks. The

study examines whether changes in domestic GNI per capita significantly impacts MFI port-

1 E.g. Gonzalez (2007), Krauss & Walter (2008).

2 E.g. Galema, Lensink & Spierdijk (2008), Janda & Svarovska (2009)

3 Labor force participation rates, inflation, manufacturing’s share in GDP and net foreign direct in-

vestment as a fraction of GDP

53

folio quality, measured by four indicators: PaR-30, PaR-90, Write-off Ratio (WOR) and Loan

loss Rate (LLR). No evidence for a relationship between MFI portfolio quality and changes

in GNI per capita (i.e., growth) is found, except for a significant relationship between growth

and PaR-30. Moreover, Gonzalez (2007, p.4) notices that PaR indicators might be “affected by

very recent economic events [e.g., growth of the same year], while WOR and LLR may be affected

by last’s year growth.” Considering such a differed effect, Gonzalez (2007) controls both for

growth and one period lag of growth. His model, however, doesn’t provide statistical signi-

ficance of such a differed effect.

In short, several empirical studies have put emphasis on MFI/MIV performance aspects and

diversification issues as well as macroeconomic factors. However the question on what

country risk factors drive portfolio quality is to some extent missing in the empirical micro-

finance literature. In particular, no empirical study addresses the impact of an interest rate

ceiling policy on portfolio quality.

6.1.2 Research questions and econometrical model

This study aims at investigating the impact of country-specific factors on MFI portfolio qual-

ity underlying GP’s investments. Special focus will be given to the role of interest rate ceil-

ings in this context. For a matter of clarity, when referring to variables specific to a country,

“country risk factors” is used as a generic term, even for variables that might not at first

sight encompass solely risk aspects.

The research questions ensue as follows:

� What country risk factors affect MFI portfolio quality underlying GP’s MIVs?

� Does an interest rate ceiling policy adversely impact portfolio quality?

The scope of study is solely limited on country-specific aspects. While the purpose of the

model is not to assess a domestic “banking crisis”, the study will enlighten country risk fac-

tors impacting microfinance from an investor’s viewpoint, specifically considering a MIV

portfolio investing in Latin America. The study stands from an investor’s viewpoint with a

risk management approach. Since portfolio quality indicators encompass the default and the

risk of default measures of an MFI, exclusively those indicators are considered.

The econometric model states as follows:

-.� +/012/34/ 5673418491 = ��:141614/�73 .7;1/0:41 + �/6�108 =4:> .7;1/0:91 , (6.1)

where the indexes i, j and t stand for MFI, country and time, respectively. The model also

controls for a time variable and country dummies.

54

It is argued that all explanatory variables in the model are exogenous (i.e. no endogeneity

problem with feedback effects exists) and therefore ordinary least squares (OLS) can be used

In particular, it is assumed that MFIs from a macroeconomic viewpoint have no influence on

country risk aspects. Indeed, Ahlin & Lin (2006) provide evidence against a reverse causality

considering growth effect on institutional factors. Furthermore, an MFI portfolio quality

may not have an impact on the institutional factors considered in the model; meaning the

latter should be chosen carefully to avoid a reverse causality. Trivially, the age of an MFI

might have an effect on its portfolio quality, but not the other way around. But, institutional

factors such as return on equity and leverage for instance are not clear, and a reverse causali-

ty may occur. In short, OLS fits well with regards to the purpose of the present research.

6.2 Selection of variables

This section is divided in three sub-sections dealing with portfolio quality measures in 6.2.1,

with country risk factors in 6.2.2, and with institutional characteristics in 6.2.3.

6.2.1 Portfolio quality

The study investigates the proposed research questions for the following portfolio quality

variables: PaR-30, PaR-90, WOR, (PaR-30+WOR), Loan Loss Rate (LLR), and Risk Coverage

Ratio (RCR). These six measures are used as the dependent variables in this research. It is

assumed that this list is exhaustive - neither the MIX, nor MicroRate considers other indica-

tors to reflect the quality of a MFI portfolio.

Dependent variable 1: Portfolio at Risk > 30 days Ratio (PaR-30)

Following the MIX, a portfolio at risk is defined as “the value of all loans outstanding that have

one or more installments of principal past due more than […] days. This includes the entire unpaid

principal balance, including both the past due and future installments, but not accrued interest. It

also includes loans that have been restructured or rescheduled.”1

The ratio is considered as follows: PaR-30 = Portfolio at Risk > 30 days/ Gross Loan Portfolio

(cf. section 4.2), expressed in %. Hence, PaR-30 is a measure of risk of default (Gonzalez,

2007).

Dependent variable 2: Portfolio at Risk > 90 days Ratio (PaR-90)

PaR-90 is equivalent to PaR-30, however with arrears over 90 days. The correlation between

these indicators is 0.86 (Gonzalez, 2007).

1 Website of the MIX, section: “Glossary”, http://www.mixmarket.org (Accessed on March 15, 2010).

55

Dependent variable 3: Write-Off Ratio (WOR)

According to the MIX, a write-off is the “total amount of loans written off during the period. A

write-off is an accounting procedure that removes the outstanding balance of the loan from the Loan

Portfolio and from the Impairment Loss Allowance when these loans are recognized as uncollecta-

ble.”1

Expressed in %, WOR is a ratio defined by the write-offs over the average gross loan portfo-

lio. In other words it is a measure of default (Gonzalez, 2007).

Dependent variable 4: (PaR-30 + WOR)

To the author’s knowledge, no empirical study has addressed a model using (PaR-30 +

WOR). It might seem unconventional to add them and create a new variable, since their re-

spective denominators slightly differ (GLP and Average GLP). However, MicroRate & IADB

(2003, p.7-13) propose to take into account a possible manipulation from an MFI that would

like to “improve” its financial statements, thus adding WOR to PaR-30. In fact, WOR “serve

as a control indicator that will allow better understanding of PaR.” (MicroRate & IADB, 2003,

p.13).

Dependent variable 5: Loan Loss Rate (LLR)

LLR is given by: (Write-offs - Value of Loans Recovered)/ Average GLP

WOR and LLR are closely related by definition. This is confirmed in the data with a almost

perfect correlation of 99% (Gonzalez, 2007).

Dependent variable 6: Risk Coverage Ratio (RCR)

RCR is given by: Impairment Loss Allowance/ PaR-30

Where the MIX defines the Impairment Loss Allowance as “the non-cash expense calculated as

a percentage of the value of the loan portfolio that is at risk of default. This value is used to create or

increase the impairment loss allowance on the balance sheet.”2

According to MicroRate & IADB (2003, p.11), RCR “gives an indication of how prepared an insti-

tution is for a worst-case scenario.”



56

6.2.2 Country risk factors

In the absence of comprehensive theory adapted to microfinance, an exhaustive classifica-

tion is necessary in order to make a review of the variables considered as “country risk”. The

latter is proposed to be categorized in four dimensions not necessarily independent, but a

purpose is to avoid recurrence amongst them1: socio-political risk, economic risk, and finan-

cial system.

Socio-political risk

According to Bouchet & al. (2004, p.16), a social-political risk may take three distinct forms:

� Political: “Democratic or non-democratic change in the government”

� Government policy: “Change in the policy of the local authorities”

� Social: “Social movement intending to influence foreign business or host country

policy”

This structure fits well for the context of this study. Specifically, an index for political stabili-

ty is used, and interest rate ceilings-related variables are taken within “social-political risk”

category.

Variable 1: Political Stability and Absence of Violence (PV)

PV is a component of the “Worldwide Governance Indicators”, an index developed by the

World Bank2. PV captures “perceptions of the likelihood that the government will be destabilized or

overthrown by unconstitutional or violent means, including politically-motivated violence and terror-

ism.” (Kaufmann & al. 2009, p.6). Figure 6.1 presents country PV scoring values.

1 An “impressive” amount of country risk indexes and sub-indexes exist through the web. Only (sub)-

indexes and variables a priori relevant for a microfinance context are considered.

2 Website of the World Bank, section: “Governance – WGI”, http://info.worldbank.org (Accessed on

March 15, 2010).

57

Figure 6.1: Cross-Country Comparisons of PV and standard errors

Source: Kaufmann & al. (2009)

Countries are classified on the horizontal axis in ascending order according to their point

estimates as of 2008; the vertical axis gives the country scoring value (“Governance Rating”)

and the associated 90% confidence intervals.

PV variable scales from -2.5 (politically instable country) to 2.5 (stable country).

Variables 2-4: Interest rate ceilings (IRC, IRC YEAR and IRC YEAR+1)

The “interest rate ceilings” variables are treated as dummies.

IRC: For a given country and year when such a policy is observed. According to CGAP

(2004a), Nicaragua introduced a “usury rate” in 2001 and Ecuador as well. However, for the

latter, “microfinance lenders are excluded from interest rate ceilings, or are authorized to charge addi-

tional fees.” (CGAP, 2004a, p.9). But as the Ecuadorian authorities introduced a ceiling on

effective interest rates encompassing also MFIs in 2007, thus, 2007 is the year to take into

consideration for Ecuador in this research (The Economist Intelligence Unit, 2007). CGAP

(2004a) specifies a ceiling policy for Bolivia introduced in 2004; specifically, NBFIs “cannot

lend individuals or groups a value above 1% of their net equity without a guarantee. The exception

58

is a housing credit, in which case NBFIs cannot lend above 5% or 10%.”1 Thus, this research takes

into account Bolivia as a country enforcing an interest rate ceiling policy. Finally, Honduras

has interest rate ceilings, but a separate regulation for MFIs also exist (CGAP, 2004a). Thus,

Honduras is not taken into consideration as having an interest rate ceiling policy.

In short, from the seven countries in the data, Nicaragua has ceilings encompassing micro-

finance from 2001, Bolivia from 2004, and Ecuador from 2007.

In regards to variables related to “interest rate ceilings”, two other variables are tested in the

general econometric model 6.1, namely IRC YEAR and IRC YEAR+1. The former is a dum-

my that takes the value “1” only for the year when the policy is enforced. Correspondingly,

the latter takes the value “1” only for the year after the policy is enforced, because the im-

pact of a policy may be deferred in the timeframe.

Economic risk

Five macroeconomic variables are considered; Inflation, GDP per capita, GDP growth rate,

Growth volatility over a 5‐year interval, and Growth volatility over a 3‐year interval. Prima-

ry data is gathered from the IMF website2.

Variable 3: Inflation (INFL)

Following Ahlin & Lin (2006), inflation is added to the model. Even if Ahlin & Lin (2006)

find overall little evidence of an inflation effect, a significant result is observed in regards to

the Write-off Ratio (WOR) in one case. While their primary result suggests that inflation is

associated with a lower WOR, this effect disappears after a robustness check. Moreover, they

show that a high inflation is associated with a higher PaR-30 in some specifications. Indeed,

“it makes sense that high inflation would give borrowers incentives to delay loan repayment (raising

PaR-30) while also perhaps enabling more borrowers eventually to repay (lowering WOR).” (Ahlin

& Lin, 2006, p.23). Inflation at end of period consumer prices is considered. It is calculated as

the year-on-year % change of the December values.

Variable 4: GDP per capita (LNGDP)

GDP expressed in current $US per person is considered. LNGDP being useful for variable 5,

as “growth rates (cf. variable 5) corrected for population growth better reflect prosperity changes within

1 Website of the Center for Financial Inclusion, http://www.centerforfinancialinclusion.org (Accessed

on March 15, 2010).

2 Website of the International Monetary Fund, section: “Data and Statistics”, http://imf.org (Accessed

on March 15, 2010).

59

regions than aggregate regional growth rates.” (Crucq & Hemminga, 2007, p.38). This variable is

taken in Logs.

Variable 5: GDP growth rate (GROWTH)

The model includes the annual growth rate of GDP per capita. As mentioned in the previous

sub-section, several studies show a significant relationship between MFI portfolio quality

measures and GROWTH. Moreover, following Ahlin & Lin (2006, p.7), “the question of how

growth correlates with representative firms’ performance may appear uninterestingly obvious. In the

case of microfinance – given its operation among economically marginal clientele, its concentration in

informal sectors, its frequent reliance on local markets, and its common non-profit status – the answer

seems far from obvious a priori.” Enhancing their model with different specifications,

GROWTH might affect negatively WOR and PaR-30, thus positively impact an MFI portfolio

quality.

Variables 6-7: Growth volatility (GROWTH SDTV5 and GROWTH SDTV3)

Growth volatility is measured by the standard deviation of real GDP per capita growth rate

over a 5‐year interval. The standard deviation measure of volatility is a commonly used in

the economic growth literature. Considering growth and volatility figures of developing

countries over the last 3 decades, Wermelinger (2009, p.1-2) notices “large growth spurts whose

positive effects are counterbalanced during sharp recessions in subsequent periods, and thus growth is

overall more volatile and unstable in poorer countries compared to growth in more developed re-

gions.” Thus, growth volatility proxies well the stability of an economy.

Moreover, an effect on MFI portfolio quality is plausible, since empirical studies confirm

that volatility is negatively related to average growth (e.g., Ramey & Ramey, 1995) “and that the

direction of causality goes from volatility to growth” (e.g., Mobarak, 2005; Wermelinger, 2009,

p.2).

According to Yang (2008), the use of a 5‐year interval – instead of longer intervals – allows

for more variation of the volatility numbers over time. Growth volatility over a 3-year inter-

val is also considered in this research.

Financial system

Three variables are considered Foreign exchange volatility over a 5‐year interval, Foreign

exchange volatility over a 3‐year interval, and Private credit bureau coverage.

Variables 8-9: Foreign exchange volatility (FX SDTV5 and FX SDTV3)

Foreign exchange volatility is measured by the standard deviation of real foreign exchange

rates change over a 5‐year interval. A 3-year interval foreign exchange is also considered.

60

Primary data is gathered from the United States Department of Agriculture1. “Real annual

country exchange rates (local currency per $US), derived by averaging 12 monthly real ex-

change rates.” Thus, an intermediary step before calculating the standard deviation is to

take the change in the foreign exchange rate for a given year against the previous year.

As mentioned in previous chapters, foreign investments fund predominantly MFIs in hard

currency, exposing MFIs to vulnerability in regards to their domestic financial system stabil-

ity. However, most of MFIs composing the panel of the study neither disclose publically

their capital structure, nor make any historical data available on a possible hedging of their

debt to match their assets. GP’s MIVs may adopt such a hedging strategy, but no informa-

tion is provided. Besides, the purpose of this “country risk” category is to proxy the financial

stability surrounding MFIs. Crabb (2004, p.54) observes that “during strong economic condi-

tions, [foreign exchange] volatility tends to be low, but geopolitical tensions and changes in capital

flows can quickly lead to higher volatility and greater risk of loss.”

Variable 10: Private credit bureau coverage (CRED)

As mentioned in sub-section 5.4.2, credit bureaus reduce information asymmetry between

lenders and micro-borrowers, and might decrease the risk of lending. An effect on MFI port-

folio quality is not excluded, thus CRED could proxy the soundness of the financial system

from a domestic perspective. Data is retrieved from “Doing Business”, an IFC-related

project. Data set is incomplete, but years from 2003-2008 are practically covered.

“The private credit bureau coverage indicator reports the number of individuals and firms listed by a

private credit bureau with information on repayment history, unpaid debts or credit outstanding from

the past 5 years. The number is expressed as a percentage of the adult population. If no private bureau

operates, the coverage value is 0.”2

6.2.3 Institutional factors

While keeping in mind to avoid a “reverse causality” (cf. sub-section 6.1.2), this sub-section

provides a brief overview on MFI independent variables selected. Primary data is gathered

from the MIX for all variables.

1Website of the United States Department of Agriculture, section: “Data and Statistics”,

http://www.usda.gov (Accessed on March 15, 2010)

2 Website of Doing Business (IFC), http://www.doingbusiness.org (Accessed on March 15, 2010).

61

Variable 11: Age (AGE)

AGE is calculated for each period considering the establishment year as focal point. Ahlin &

Lin (2006) find an effect of AGE on WOR at significance level 10% in one case. Besides port-

folio quality measures, they assess other MFI variables - falling beyond the concern of this

research – and conclude that “the importance of institution-specific age effects makes clear that

much of success originates within the institution.” (Ahlin & Lin, 2006, p.28)

Variable 12: Regulation (REG)

Treated as a dummy, REG indicates whether an MFI is regulated or not. A possible effect on

portfolio quality is not to be excluded. A priori a regulated MFI could be more conservative

on lending attributions, thus have lower defaults and risk of default. However, the regulato-

ry framework is different across countries, so its effect remains unclear.

Variable 13: Current legal status (STATUS)

STATUS is also a dummy and controls for the type of MFI. The panel is composed of 19

NGOs, seven NBFIs, one bank (FINCA in Ecuador), and one Cooperative (COOP 20 in Nica-

ragua). STATUS takes 0 if it’s an NGO, 1 otherwise. A minor limitation is the availability of

historical data; STATUS may have been different in the past due to an MFI transformation

(e.g., from NGO to NBFI), the research assume no change in this dummy for a specific MFI.

Specific proprieties of MFI type can be consulted in sub-section 2.2.2.

Variable 14: Gross loan portfolio (LNGLP)

Similarly as GDP per capita, the natural logarithm is considered for GLP. Moreover, defini-

tion for GLP, and MFI indicators in general, varies slightly across practitioners. Since the

data is gathered from the MIX, the definition is adopted as well. GLP is defined as “all out-

standing principals due for all outstanding client loans. This includes current, delinquent, and rene-

gotiated loans, but not loans that have been written off. It does not include interest receivable.”1 GLP

reflects the size of the MFI and is commonly used among practitioners and academics.

Variable 15: GLP growth (GLPGROWTH)

Expressed in percentage on an annual basis; GLP growth reflects the economic expansion of

an MFI that might be negatively correlated with PaR, for example.

Variable 16: Average Loan Balance per Borrower (OUTREACH)

OUTREACH is given by “Adjusted Average Outstanding Balance/GNI per Capita” (cf. Ta-

ble 2.1). A common measure used as a proxy for the depth of outreach to micro-borrowers in

regards to social performance (e.g., Gutiérrez Nieto & Serrano Cinca, 2006; Cull & al. 2007).


62

Furthermore, Gonzalez (2007) controls portfolio quality for other independent institutional

variables (highly suspected to be endogenous).

This study following model 6.1 includes no other institutional indicator due to this feedback

problem.

6.3 Empirical results

6.3.1 Data and descriptive statistics

Data for MFI variables is obtained from the MIX database as of March 2010. All available

data is gathered leading to a maximum of 174 observations for PaR-30 and the Risk Cover-

age Ratio to a minimum of 131 observations for PaR-90, from 1997 to 2008. Data is assumed

to be of high quality as 24 out of 28 MFIs composing the panel are noted with “5 diamonds”;

the highest grade the MIX is attributing in regards to quality of information (e.g., audited

financial statements). The remaining 4 MFIs have “4 diamonds”, which still reflects reliable

information. Moreover, all MFIs have “90-100% operations comprised by microfinance”,

meaning the panel data is composed exclusively with stricto sensu MFIs. Indeed, some MFIs

claim to provide microfinance services, but in fact those are e.g., banks providing low-

income borrowers with consumer loans, or with financial products not being microfinance

as defined in chapter 2. Moreover, for the reason of singularity the country dummy for Mex-

ico is retrieved from the dataset.

The descriptive statistics are provided in Appendix V. In regards to MFI variables, OUT-

REACH is particularly pertinent; all the panel is characterized by a low average loan bal-

ance, i.e., less than 250% of GNI per capita (cf. Chapter 2, Table 2.1). The maximum value of

the OUTREACH variable is 201.6% (2.016), while the mean is approximately 40%. An impor-

tant finding, because GP is putting great emphasis on the social performance issue. With

respect to outliers, one can notice that the LNGDP variable takes the minimum with Nicara-

gua and the maximum with Mexico; severe outlier values. Instead of excluding data of Nica-

ragua and Mexico, a robustness check by excluding the LNGDP variable could be per-

formed. Regarding the PV index, Bolivia is with a value of -1.13 in 2006 the most instable

country, whereas El Salvador in 2002 reported the highest number.

The correlation matrix is provided in Appendix VI1. Correlations over 0.5 and under -0.5 are

highlighted in yellow. In regards to country risk factors, PV is negatively correlated with

GROWTH and LNGDP at more than 50%. Growth volatility over a 5-year interval is corre-

lated with both measures of foreign exchange volatility, but growth volatility over a 3-year

interval doesn’t show a high correlation with them. Furthermore, the result for LLR and

1 For a matter of clarity, the left-hand features column is kept the “split” matrix.

63

WOR is 98.6% being in line with Gonzalez (2007). By construction, PAR-30 and PAR-90 pro-

vide a high linear relationship. (PAR-30 + WOR) highly correlates with WOR, PAR-30, and

PaR-90, but WOR does correlate neither with PAR-30, nor with PAR-90.

6.3.2 Regression results

The results are presented by MFI portfolio quality indicator, where two specifications are

treated for a robustness check. The first one incorporates all variables, while the second one

retrieves time effect and country dummies. It must be emphasized that the first specification

modelizes better the reality; indeed country dummies particularly are catching country-

specific effects that country risk factors don’t. Considering six dependent variables and two

specifications, 12 approaches to regress country risk factors on portfolio quality are tackled.

a) PAR-30 (Appendix VII &VIII)

� In the first specification, GROWTH and PV affect significantly PAR-30. Both have a

negative coefficient; e.g., the higher GROWTH, the lower PAR-30. The economic

progress and a politically stable country might avoid delays and disorder in regards

to loan repayments. Inflation and growth volatility over a 3-year interval are signifi-

cant but at lower levels.

� In order to check the robustness of the model, time effect and country dummies are

retrieved. The results change considerably. The model suggests significance only for

institutional factors, namely LNGLP and GLPGROWTH.

� While the first specification implies strong effects of macroeconomic variables and of

the index that should proxy socio-political risk, the robustness check cannot confirm

such effects.

b) PAR-90 (Appendix IX &X)

� In the first specification, several country risk factors are highly significant; namely

GROWTH, INFL, GROWTH SDTV3, FX SDTV3, FX SDTV5, IRC, and IRC YEAR +1.

� The robustness check confirms solely IRC; that is a major implication for the re-

search. PAR-90 is the only portfolio quality proxy to be significantly affected by such

a policy. On the other hand, the IRC coefficient is negative, suggesting a negative re-

lationship between PAR-90 and IRC. Recall that IRC is a dummy variable that takes

“1” for the years that an interest rate ceiling policy is occurring. This effect is trans-

lated into a higher portfolio quality for an MFI.

64

c) WOR (Appendix XI &XII)

� Both specifications for WOR provide no significance for country risk factors, except

in two cases; GROWTH has significance at 90% when regressing with time effect and

country dummies. GROWTH SDTV5 has a high significance as well, it might affect

WOR, but its coefficient is negatively related; i.e., the higher the volatility, the lower

the write-off ratio. WOR as a stand-alone measure might not be so reliable; from a

practitioner approach, NGOs particularly resist writing off their seriously delinquent loans

because, they argue, “collection efforts continue”. (MicroRate & IADB, 2003, p.13).

d) (PAR-30 + WOR) (Appendix XIII &XIV)

� In the first specification, GROWTH SDTV5 and GROWTH may affect (PAR-30 +

WOR), as well as PV. But significance is not confirmed in the robustness check.

e) LLR (Appendix XV &XVI)

� In the first specification, only GROWTH SDTV5 as a country risk factor may have a

negative impact. The robustness check confirms high significance for two institution-

al factors; namely OUTREACH and GLPGROWTH. The former is significant also

when regressing against WOR.

f) RCR (Appendix XVII &XVIII)

� In the first specification, country risk factors related to economic and foreign ex-

change stability (GROWTH SDTV5, FX SDTV5 and FX SDTV3) have high signific-

ance. They are not confirmed in the second specification; however in regards to insti-

tutional factors, OUTREACH and GLPGROWTH are significant in both specifica-

tions. Moreover, through the entire research, OUTREACH is often significant and

negatively related to the dependent variables; i.e. the larger the loan size, the better

the portfolio quality.

65

6.3.3 Conclusion

Recall the research questions that ensue as follows:

� What country risk factors affect MFI portfolio quality underlying GP’s MIVs?

� Does an interest rate ceiling policy adversely impact portfolio quality?

� First, country risk factors are seldom confirmed by a robustness check, but often the

second specification provides significance of another country-related variable. In

specifications for the risk coverage ratio for instance, the first specification finds high

significance for three macroeconomic stability variables; while checking for robust-

ness, their significance disappears, but growth “appears” in the second specification

with a high significant relationship to a portfolio quality proxy. The model in both

specifications tends to be explained by country risk factors; might be macroeconomic

variables, or might be a country-specific index. In short, growth, growth volatility,

foreign exchange volatility, and political stability might significantly affect MFI port-

folio quality underlying GP’s MIVs. On the other hand, a country-indicator such as

private credit bureau coverage (CRED) is plausibly excluded to affect the present

portfolio underlying.

Second, an adverse impact of interest rate ceilings on portfolio quality measures has not

been observed. Conversely, interest rate ceilings may lower portfolio at risk with arrears

over 90 days. Treating interest rate ceiling policy as a dummy fits well the context; however,

as the study faces annual data, controlling for such a policy is a more complex issue.

To conclude, the quality of information might play a role in regards to the interest rate ceil-

ings; in some countries such a policy doesn’t systematically encompass MFIs (e.g., Ecuador

from 2004 to 2007), in others microfinance is targeted following populist arguments.

66

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Appendix I: Key principles of microfinance

1. Poor people need a variety of financial services, not just loans. In addition to credit, they

want savings, insurance, and money transfer services.

2. Microfinance is a powerful tool to fight poverty. Poor households use financial services to

raise income, build their assets, and cushion themselves against external shocks.

3. Microfinance means building financial systems that serve the poor. Microfinance will

reach its full potential only if it is integrated into a country’s mainstream financial system.

4. Microfinance can pay for itself, and must do so if it is to reach very large numbers of poor

people. Unless microfinance providers charge enough to cover their costs, they will always

be limited by the scarce and uncertain supply of subsidies from governments and donors.

5. Microfinance is about building permanent local financial institutions that can attract do-

mestic deposits, recycle them into loans, and provide other financial services.

6. Microcredit is not always the answer. Other kinds of support may work better for people

who are so destitute that they are without income or means of repayment.

7. Interest rate ceilings hurt poor people by making it harder for them to get credit. Making

many small loans costs more than making a few large ones. Interest rate ceilings prevent

microfinance institutions from covering their costs, and thereby choke off the supply of

credit for poor people.

8. The job of government is to enable financial services, not to provide them directly. Gov-

ernments can almost never do a good job of lending, but they can set a supporting policy

environment.

9. Donor funds should complement private capital, not compete with it. Donor subsides

should be temporary start-up support designed to get an institution to the point where it can

tap private funding sources, such as deposits.

10. The key bottleneck is the shortage of strong institutions and managers. Donors should

focus their support on building capacity.

11. Microfinance works best when it measures—and discloses—its performance. Reporting

not only helps stakeholders judge costs and benefits, but it also improves performance. MFIs

need to produce accurate and comparable reporting on financial performance (e.g., loan

repayment and cost recovery) as well as social performance (e.g., number and poverty level

of clients being served).

Source: CGAP (2004)

75

Appendix II: Advantages and disadvantages of debt versus equity

Capital

structure

Advantages Disavantages

Debt Often, there is greater local demand for

debt than equity.

Greater administrative efficiency for the

MFI (e.g., easier to negotiate, shorter

negotiation time, less intensive relation-

ship with lenders than with equity

holders.

Interest payments may be deductible as

expense for tax purposes.

MFI does not lose control of the enter-

prise.

Regulatory constraints to leve-

rage

Increased financial risk attached

to managing higher leverage.

Debt agreements can limit the

MFI’s alternatives when prob-

lems arise.

Equity Longer-term commitment of funds (al-

though some investors may require exit

strategy)

Shareholders can bring additional bene-

fits to the MFI:

1. Financial and business expertise of

shareholders can help develop MFI ca-

pacity

2. Some shareholders may be able to

respond to emergencies with additional

capital

3. Some international and other share-

holders can bring prestige, which can

improve the reputation and risk rating

of the MFI, facilitate access to credit

lines and help with regulators

Conservative dividend policy can facili-

tate capital accumulation

Difficulties in identifying equity

partners who are fully dedicated

to the MFI’s social mission

Disputes may arise with new

investors in numerous areas,

such as exit strategy, personnel

appointments, board control

and dividend policy

The negotiation process can be

longer, require greater man-

agement involvement, and re-

sult in a longer documentation

process

Source: based on (Maisch & al. 2006)

76

Appendix III: Top 10 MIV in 2008

Source: MicroRate (2009c)

Appendix IV: MIV survey participants

Source: MicroRate (2009a)

77

Appendix V: Descriptive statistics

Variable Observations Minimum Maximum Mean Std. deviation

PAR30 174 0.000 0.414 0.043 0.051

PAR90 174 0.000 0.409 0.022 0.033

WOR 174 0.000 0.417 0.017 0.036

(PAR30+WOR) 174 0.003 0.494 0.058 0.066

LLR 174 -0.034 0.375 0.015 0.033

RCR 174 -0.684 37.536 2.189 3.882

AGE 174 1.000 36.000 11.920 5.741

STATUS 174 0.000 1.000 0.253 0.436

REG 174 0.000 1.000 0.121 0.327

LNGLP 174 11.847 18.627 15.592 1.161

GLPGROWTH 174 -0.190 1.096 0.300 0.251

OUTREACH 174 0.037 2.016 0.397 0.372

GROWTH 174 0.004 0.098 0.044 0.020

LNGDP 174 6.588 9.230 7.546 0.648

INFL 174 0.002 0.377 0.064 0.049

GROWTH SDTV5 174 0.266 4.695 1.607 0.835

GROWTH SDTV3 174 0.190 6.119 1.214 0.771

PV 174 -1.130 0.140 -0.468 0.347

IRC 174 0.000 1.000 0.489 0.501

IRC YEAR 174 0.000 1.000 0.057 0.233

IRC YEAR +1 174 0.000 1.000 0.057 0.233

CRED 174 0.000 1.000 0.288 0.304

FX SDTV3 174 0.000 0.354 0.026 0.046

FX SDTV5 174 0.000 0.270 0.036 0.057

Obs. Year 174 1997.000 2008.000 2004.736 2.605

Honduras 174 0.000 1.000 0.080 0.273

El Salvador 174 0.000 1.000 0.155 0.363

Nicaragua 174 0.000 1.000 0.328 0.471

Ecuador 174 0.000 1.000 0.155 0.363

Peru 174 0.000 1.000 0.121 0.327

Bolivia 174 0.000 1.000 0.144 0.352

Source: own research

78

Appendix VI: Correlations

Cells highlighted in yellow indicate correlation over 0.5 or under -0.5. A correlation ma-

trix is symmetric, thus only the bottom part is considered.

Blue font cells are the dependent variables.

Variables AGE STATUS REG LNGLP GLPGROWTH OUTREACH GROWTH LNGDP INFL G. SDTV5

AGE 1.000 0.142 0.051 0.574 0.033 0.574 -0.017 0.139 -0.018 -0.220

STATUS 0.142 1.000 0.637 0.274 -0.044 0.171 -0.072 0.315 -0.140 -0.349

REG 0.051 0.637 1.000 0.266 -0.017 -0.103 0.178 0.208 -0.112 -0.103

LNGLP 0.574 0.274 0.266 1.000 0.103 0.478 -0.048 0.158 0.019 -0.336

GLPGROWTH 0.033 -0.044 -0.017 0.103 1.000 -0.101 0.262 0.124 -0.094 0.114

OUTREACH 0.574 0.171 -0.103 0.478 -0.101 1.000 -0.217 -0.353 0.298 -0.202

GROWTH -0.017 -0.072 0.178 -0.048 0.262 -0.217 1.000 0.269 -0.076 0.113

LNGDP 0.139 0.315 0.208 0.158 0.124 -0.353 0.269 1.000 -0.482 -0.098

INFL -0.018 -0.140 -0.112 0.019 -0.094 0.298 -0.076 -0.482 1.000 0.133

GROWTH SDTV5-0.220 -0.349 -0.103 -0.336 0.114 -0.202 0.113 -0.098 0.133 1.000

GROWTH SDTV3-0.178 -0.234 -0.037 -0.228 -0.042 -0.143 0.129 -0.086 0.104 0.476

PV 0.028 0.326 -0.020 0.078 -0.174 0.405 -0.552 -0.424 0.153 -0.371

IRC 0.042 -0.304 -0.115 0.183 0.068 0.230 -0.113 -0.542 0.333 -0.110

IRC YEAR -0.031 -0.087 -0.016 0.009 0.055 -0.066 -0.169 -0.023 -0.147 0.021

IRC YEAR +1 0.012 -0.087 -0.016 0.086 0.157 -0.057 -0.077 0.004 -0.058 0.119

CRED 0.296 0.480 0.157 0.392 0.041 0.192 -0.146 0.407 -0.038 -0.438

FX SDTV3 -0.090 -0.161 -0.071 -0.121 0.051 -0.113 0.177 0.122 0.442 0.508

FX SDTV5 -0.150 -0.168 -0.049 -0.189 0.124 -0.178 0.163 0.115 0.243 0.673

Obs. Year 0.333 0.197 0.167 0.531 0.244 0.158 0.302 0.358 0.095 -0.234

Honduras -0.129 0.071 0.215 -0.287 0.013 -0.152 0.209 -0.126 0.094 -0.084

El Salvador 0.175 0.737 0.182 0.194 -0.053 0.192 -0.301 0.325 -0.187 -0.413

Nicaragua -0.022 -0.322 -0.259 0.087 -0.079 0.465 -0.263 -0.872 0.496 -0.007

Ecuador -0.108 -0.067 0.085 -0.033 0.212 -0.271 0.132 0.254 0.002 0.553

Peru 0.048 -0.053 0.079 -0.112 -0.006 -0.155 0.574 0.322 -0.268 0.154

Bolivia 0.032 -0.238 -0.152 0.058 -0.039 -0.231 -0.140 0.244 -0.275 -0.205

PAR30 -0.037 0.067 0.036 -0.206 -0.256 0.069 -0.016 -0.151 0.130 -0.029

PAR90 0.093 0.047 0.066 -0.004 -0.215 0.009 -0.091 -0.024 0.018 -0.006

WOR 0.004 -0.010 -0.027 -0.133 -0.261 -0.096 -0.052 -0.008 -0.049 -0.027

(PAR30+WOR)-0.015 0.044 0.009 -0.206 -0.310 0.009 -0.039 -0.119 0.071 -0.042

LLR -0.040 -0.013 -0.023 -0.137 -0.256 -0.104 -0.086 -0.041 -0.026 -0.032

RCR -0.004 -0.197 -0.094 -0.044 0.210 -0.208 -0.105 0.029 -0.094 0.196

79

Variables G. SDTV3 PV IRC IRC YEAR IRC YEAR +1 CRED FX SDTV3 FX SDTV5 Obs. Year Honduras

AGE -0.178 0.028 0.042 -0.031 0.012 0.296 -0.090 -0.150 0.333 -0.129

STATUS -0.234 0.326 -0.304 -0.087 -0.087 0.480 -0.161 -0.168 0.197 0.071

REG -0.037 -0.020 -0.115 -0.016 -0.016 0.157 -0.071 -0.049 0.167 0.215

LNGLP -0.228 0.078 0.183 0.009 0.086 0.392 -0.121 -0.189 0.531 -0.287

GLPGROWTH -0.042 -0.174 0.068 0.055 0.157 0.041 0.051 0.124 0.244 0.013

OUTREACH -0.143 0.405 0.230 -0.066 -0.057 0.192 -0.113 -0.178 0.158 -0.152

GROWTH 0.129 -0.552 -0.113 -0.169 -0.077 -0.146 0.177 0.163 0.302 0.209

LNGDP -0.086 -0.424 -0.542 -0.023 0.004 0.407 0.122 0.115 0.358 -0.126

INFL 0.104 0.153 0.333 -0.147 -0.058 -0.038 0.442 0.243 0.095 0.094

GROWTH SDTV5 0.476 -0.371 -0.110 0.021 0.119 -0.438 0.508 0.673 -0.234 -0.084

GROWTH SDTV3 1.000 -0.182 -0.121 0.162 0.152 -0.328 0.248 0.272 -0.288 -0.140

PV -0.182 1.000 0.047 -0.045 -0.087 0.222 -0.377 -0.451 -0.232 -0.019

IRC -0.121 0.047 1.000 0.253 0.253 -0.149 -0.077 -0.218 0.294 0.303

IRC YEAR 0.162 -0.045 0.253 1.000 -0.061 -0.067 -0.114 -0.085 -0.070 -0.073

IRC YEAR +1 0.152 -0.087 0.253 -0.061 1.000 -0.042 -0.059 -0.096 0.025 -0.073

CRED -0.328 0.222 -0.149 -0.067 -0.042 1.000 -0.182 -0.257 0.528 -0.038

FX SDTV3 0.248 -0.377 -0.077 -0.114 -0.059 -0.182 1.000 0.824 0.015 -0.097

FX SDTV5 0.272 -0.451 -0.218 -0.085 -0.096 -0.257 0.824 1.000 -0.080 -0.112

Obs. Year -0.288 -0.232 0.294 -0.070 0.025 0.528 0.015 -0.080 1.000 0.095

Honduras -0.140 -0.019 0.303 -0.073 -0.073 -0.038 -0.097 -0.112 0.095 1.000

El Salvador -0.287 0.530 -0.419 -0.106 -0.106 0.532 -0.166 -0.203 0.056 -0.127

Nicaragua 0.044 0.424 0.543 0.038 0.038 -0.240 -0.133 -0.175 -0.127 -0.206

Ecuador 0.448 -0.493 -0.165 0.167 0.167 -0.125 0.392 0.658 0.050 -0.127

Peru 0.052 -0.414 -0.362 -0.091 -0.091 0.021 -0.024 -0.058 0.160 -0.110

Bolivia -0.171 -0.197 0.026 0.040 0.040 -0.151 0.042 -0.100 -0.204 -0.121

PAR30 0.038 0.075 -0.041 -0.078 -0.102 -0.004 -0.051 -0.065 -0.138 0.306

PAR90 0.023 0.022 -0.131 -0.071 -0.074 0.052 -0.038 -0.020 -0.083 0.011

WOR -0.046 0.030 -0.088 -0.013 0.005 -0.002 -0.049 -0.076 -0.112 0.009

(PAR30+WOR) -0.012 0.072 -0.068 -0.067 -0.070 0.009 -0.074 -0.094 -0.137 0.232

LLR -0.041 0.065 -0.058 -0.004 0.013 -0.016 -0.046 -0.071 -0.127 0.016

RCR -0.039 -0.138 0.014 0.023 0.032 -0.198 0.091 0.177 -0.120 -0.105

Variables El Salvador Nicaragua Ecuador Peru Bolivia PAR30 PAR90 WOR (PAR30+WOR) LLR RCR

AGE 0.175 -0.022 -0.108 0.048 0.032 -0.037 0.093 0.004 -0.015 -0.040 -0.004

STATUS 0.737 -0.322 -0.067 -0.053 -0.238 0.067 0.047 -0.010 0.044 -0.013 -0.197

REG 0.182 -0.259 0.085 0.079 -0.152 0.036 0.066 -0.027 0.009 -0.023 -0.094

LNGLP 0.194 0.087 -0.033 -0.112 0.058 -0.206 -0.004 -0.133 -0.206 -0.137 -0.044

GLPGROWTH -0.053 -0.079 0.212 -0.006 -0.039 -0.256 -0.215 -0.261 -0.310 -0.256 0.210

OUTREACH 0.192 0.465 -0.271 -0.155 -0.231 0.069 0.009 -0.096 0.009 -0.104 -0.208

GROWTH -0.301 -0.263 0.132 0.574 -0.140 -0.016 -0.091 -0.052 -0.039 -0.086 -0.105

LNGDP 0.325 -0.872 0.254 0.322 0.244 -0.151 -0.024 -0.008 -0.119 -0.041 0.029

INFL -0.187 0.496 0.002 -0.268 -0.275 0.130 0.018 -0.049 0.071 -0.026 -0.094

GROWTH SDTV5 -0.413 -0.007 0.553 0.154 -0.205 -0.029 -0.006 -0.027 -0.042 -0.032 0.196

GROWTH SDTV3 -0.287 0.044 0.448 0.052 -0.171 0.038 0.023 -0.046 -0.012 -0.041 -0.039

PV 0.530 0.424 -0.493 -0.414 -0.197 0.075 0.022 0.030 0.072 0.065 -0.138

IRC -0.419 0.543 -0.165 -0.362 0.026 -0.041 -0.131 -0.088 -0.068 -0.058 0.014

IRC YEAR -0.106 0.038 0.167 -0.091 0.040 -0.078 -0.071 -0.013 -0.067 -0.004 0.023

IRC YEAR +1 -0.106 0.038 0.167 -0.091 0.040 -0.102 -0.074 0.005 -0.070 0.013 0.032

CRED 0.532 -0.240 -0.125 0.021 -0.151 -0.004 0.052 -0.002 0.009 -0.016 -0.198

FX SDTV3 -0.166 -0.133 0.392 -0.024 0.042 -0.051 -0.038 -0.049 -0.074 -0.046 0.091

FX SDTV5 -0.203 -0.175 0.658 -0.058 -0.100 -0.065 -0.020 -0.076 -0.094 -0.071 0.177

Obs. Year 0.056 -0.127 0.050 0.160 -0.204 -0.138 -0.083 -0.112 -0.137 -0.127 -0.120

Honduras -0.127 -0.206 -0.127 -0.110 -0.121 0.306 0.011 0.009 0.232 0.016 -0.105

El Salvador 1.000 -0.299 -0.184 -0.159 -0.176 0.108 0.081 0.020 0.094 0.018 -0.185

Nicaragua -0.299 1.000 -0.299 -0.259 -0.286 0.004 -0.022 -0.014 0.005 0.017 -0.062

Ecuador -0.184 -0.299 1.000 -0.159 -0.176 -0.193 -0.151 -0.121 -0.211 -0.118 0.095

Peru -0.159 -0.259 -0.159 1.000 -0.152 0.156 0.245 0.201 0.224 0.141 -0.103

Bolivia -0.176 -0.286 -0.176 -0.152 1.000 -0.281 -0.124 -0.086 -0.269 -0.077 0.370

PAR30 0.108 0.004 -0.193 0.156 -0.281 1.000 0.662 0.174 0.844 0.167 -0.283

PAR90 0.081 -0.022 -0.151 0.245 -0.124 0.662 1.000 0.127 0.559 0.111 -0.104

WOR 0.020 -0.014 -0.121 0.201 -0.086 0.174 0.127 1.000 0.670 0.986 -0.079

(PAR30+WOR) 0.094 0.005 -0.211 0.224 -0.269 0.844 0.559 0.670 1.000 0.657 -0.254

LLR 0.018 0.017 -0.118 0.141 -0.077 0.167 0.111 0.986 0.657 1.000 -0.068

RCR -0.185 -0.062 0.095 -0.103 0.370 -0.283 -0.104 -0.079 -0.254 -0.068 1.000

80

Appendix VII: PAR-30

Goodness of fit statistics:

Observations 174.000 Significance level 1%

Sum of weights 174.000

DF 148.000 Significance level 5%

R² 0.418

Adjusted R² 0.319 Significance level 10%

MSE 0.002

RMSE 0.042

MAPE 171.285

DW 1.825

Cp 26.000

AIC -1077.919

SBC -995.783

PC 0.787

Analysis of variance:

Source DF Sum of squares Mean squares F Pr > F

Model 25 0.189 0.008 4.247 < 0.0001

Error 148 0.263 0.002

Corrected Total 173 0.452

Computed against model Y=Mean(Y)

Model parameters:

Source Value Standard error t Pr > |t| Lower bound (95%) Upper bound (95%)

Intercept 15.989 7.969 2.006 0.047 0.241 31.738

AGE 0.000 0.001 -0.309 0.757 -0.002 0.001

STATUS -0.023 0.018 -1.258 0.210 -0.060 0.013

REG 0.004 0.018 0.213 0.832 -0.032 0.040

LNGLP 0.005 0.005 0.964 0.337 -0.005 0.016

GLPGROWTH -0.021 0.015 -1.349 0.179 -0.051 0.010

OUTREACH 0.008 0.016 0.460 0.646 -0.025 0.040

GROWTH -0.666 0.312 -2.136 0.034 -1.282 -0.050

LNGDP -0.006 0.047 -0.121 0.904 -0.099 0.087

INFL 0.230 0.122 1.884 0.062 -0.011 0.472

GROWTH SDTV5 -0.011 0.008 -1.417 0.159 -0.027 0.004

GROWTH SDTV3 0.010 0.006 1.664 0.098 -0.002 0.023

PV -0.063 0.023 -2.709 0.008 -0.108 -0.017

IRC 0.023 0.022 1.044 0.298 -0.020 0.066

IRC YEAR -0.014 0.021 -0.655 0.514 -0.055 0.028

IRC YEAR +1 -0.006 0.020 -0.310 0.757 -0.046 0.033

CRED 0.001 0.018 0.075 0.940 -0.035 0.037

FX SDTV3 -0.197 0.208 -0.950 0.344 -0.607 0.213

FX SDTV5 0.203 0.190 1.067 0.287 -0.173 0.579

Obs. Year -0.008 0.004 -1.937 0.055 -0.016 0.000

Honduras 0.058 0.090 0.637 0.525 -0.121 0.236

El Salvador 0.058 0.060 0.975 0.331 -0.060 0.176

Nicaragua -0.026 0.108 -0.244 0.808 -0.239 0.186

Ecuador -0.039 0.060 -0.645 0.520 -0.157 0.080

Peru 0.051 0.061 0.842 0.401 -0.069 0.171

Bolivia -0.056 0.059 -0.954 0.342 -0.173 0.060

81

Appendix VIII: PAR-30 without time effects and country dummies





R² 0.180


MSE 0.002

RMSE 0.049

MAPE 203.702

DW 1.547

Cp 19.000

AIC -1032.289

SBC -972.267

PC 1.021


Source DF Sum of squares Mean squares F Pr > F

Model 18 0.081 0.005 1.887 0.021

Error 155 0.371 0.002



Model parameters:


Intercept 0.363 0.110 3.291 0.001 0.145 0.581

AGE 0.001 0.001 0.596 0.552 -0.001 0.002

STATUS 0.014 0.017 0.820 0.414 -0.019 0.047

REG 0.010 0.019 0.526 0.599 -0.027 0.047

LNGLP -0.015 0.005 -2.926 0.004 -0.024 -0.005

GLPGROWTH -0.037 0.017 -2.212 0.028 -0.071 -0.004

OUTREACH 0.015 0.018 0.787 0.432 -0.022 0.051

GROWTH 0.034 0.259 0.133 0.895 -0.477 0.545

LNGDP -0.016 0.014 -1.146 0.254 -0.043 0.011

INFL 0.129 0.124 1.041 0.299 -0.116 0.373

GROWTH SDTV5 -0.002 0.008 -0.282 0.778 -0.017 0.013

GROWTH SDTV3 0.002 0.006 0.336 0.737 -0.010 0.014

PV -0.029 0.020 -1.447 0.150 -0.070 0.011

IRC -0.009 0.013 -0.656 0.513 -0.034 0.017

IRC YEAR -0.006 0.019 -0.329 0.743 -0.045 0.032

IRC YEAR +1 -0.006 0.019 -0.287 0.775 -0.044 0.033

CRED 0.018 0.018 0.974 0.331 -0.018 0.054

FX SDTV3 -0.072 0.184 -0.392 0.696 -0.436 0.291

FX SDTV5 -0.073 0.157 -0.464 0.643 -0.382 0.237

82

Appendix IX: PAR-90





R² 0.553


MSE 0.001

RMSE 0.028

MAPE 224.368

DW 2.094

Cp 26.000

AIC -910.095

SBC -835.340

PC 0.668


Source DF Sum of squaresMean squares F Pr > F

Model 25 0.105 0.004 5.204 < 0.0001

Error 105 0.085 0.001



Model parameters:


Intercept 9.977 20.524 0.486 0.628 -30.717 50.672

AGE 0.000 0.001 0.079 0.937 -0.001 0.001

STATUS -0.010 0.013 -0.750 0.455 -0.036 0.016

REG 0.009 0.013 0.675 0.501 -0.018 0.036

LNGLP 0.009 0.004 2.054 0.042 0.000 0.018

GLPGROWTH 0.012 0.013 0.920 0.360 -0.014 0.038

OUTREACH -0.009 0.012 -0.741 0.460 -0.033 0.015

GROWTH -1.637 0.326 -5.023 < 0.0001 -2.284 -0.991

LNGDP -0.062 0.118 -0.527 0.600 -0.297 0.172

INFL 0.478 0.183 2.610 0.010 0.115 0.841

GROWTH SDTV5 -0.001 0.012 -0.118 0.906 -0.025 0.022

GROWTH SDTV3 0.024 0.007 3.362 0.001 0.010 0.038

PV -0.005 0.034 -0.160 0.873 -0.074 0.063

IRC 0.096 0.027 3.579 0.001 0.043 0.149

IRC YEAR -0.068 0.025 -2.696 0.008 -0.118 -0.018

IRC YEAR +1 -0.024 0.025 -0.952 0.343 -0.074 0.026

CRED 0.012 0.018 0.660 0.510 -0.024 0.047

FX SDTV3 -0.675 0.313 -2.157 0.033 -1.295 -0.055

FX SDTV5 0.964 0.276 3.494 0.001 0.417 1.511

Obs. Year -0.005 0.011 -0.443 0.658 -0.026 0.017

Honduras -0.147 0.227 -0.650 0.517 -0.598 0.303

El Salvador -0.030 0.127 -0.239 0.812 -0.282 0.221

Nicaragua -0.246 0.284 -0.865 0.389 -0.810 0.318

Ecuador -0.122 0.124 -0.983 0.328 -0.369 0.124

Peru 0.069 0.123 0.557 0.579 -0.176 0.314

Bolivia -0.094 0.134 -0.702 0.484 -0.361 0.172

83

Appendix X: PAR-90 without time effects and country dummies





R² 0.187


MSE 0.001

RMSE 0.037

MAPE 323.593

DW 1.765

Cp 19.000

AIC -845.559

SBC -790.930

PC 1.089



Model 18 0.035 0.002 1.427 0.133

Error 112 0.154 0.001



Model parameters:


Intercept 0.232 0.123 1.883 0.062 -0.012 0.477

AGE 0.001 0.001 1.504 0.135 0.000 0.003

STATUS 0.002 0.015 0.138 0.891 -0.028 0.032

REG 0.006 0.016 0.382 0.703 -0.026 0.038

LNGLP 0.000 0.005 0.034 0.973 -0.010 0.010

GLPGROWTH -0.028 0.016 -1.801 0.074 -0.059 0.003

OUTREACH -0.019 0.016 -1.217 0.226 -0.050 0.012

GROWTH -0.385 0.254 -1.515 0.133 -0.888 0.118

LNGDP -0.028 0.013 -2.070 0.041 -0.055 -0.001

INFL 0.077 0.121 0.638 0.525 -0.163 0.317

GROWTH SDTV5 0.000 0.010 -0.007 0.995 -0.021 0.020

GROWTH SDTV3 0.008 0.008 0.986 0.326 -0.008 0.025

PV -0.040 0.020 -1.967 0.052 -0.081 0.000

IRC -0.034 0.015 -2.217 0.029 -0.064 -0.004

IRC YEAR -0.012 0.022 -0.537 0.592 -0.057 0.032

IRC YEAR +1 -0.007 0.024 -0.311 0.756 -0.054 0.039

CRED 0.020 0.016 1.254 0.213 -0.012 0.052

FX SDTV3 0.251 0.225 1.117 0.266 -0.194 0.696

FX SDTV5 -0.296 0.185 -1.602 0.112 -0.663 0.070

84

Appendix XI: WOR





R² 0.278


MSE 0.001

RMSE 0.035

MAPE 320.962

DW 1.932

Cp 26.000

AIC -1024.215

SBC -944.919

PC 1.011



Model 25 0.061 0.002 2.004 0.006

Error 130 0.157 0.001



Model parameters:


Intercept 13.813 7.797 1.772 0.079 -1.612 29.239

AGE 0.002 0.001 2.163 0.032 0.000 0.003

STATUS 0.016 0.016 0.965 0.336 -0.016 0.047

REG -0.023 0.016 -1.396 0.165 -0.055 0.009

LNGLP 0.005 0.005 1.020 0.309 -0.005 0.015

GLPGROWTH -0.033 0.013 -2.457 0.015 -0.060 -0.006

OUTREACH -0.040 0.015 -2.720 0.007 -0.068 -0.011

GROWTH -0.476 0.281 -1.695 0.092 -1.031 0.079

LNGDP -0.009 0.045 -0.189 0.850 -0.098 0.081

INFL -0.008 0.120 -0.069 0.945 -0.246 0.229

GROWTH SDTV5 -0.016 0.007 -2.214 0.029 -0.030 -0.002

GROWTH SDTV3 -0.006 0.007 -0.824 0.411 -0.019 0.008

PV 0.023 0.022 1.064 0.289 -0.020 0.067

IRC 0.031 0.022 1.420 0.158 -0.012 0.074

IRC YEAR -0.015 0.020 -0.784 0.434 -0.054 0.024

IRC YEAR +1 0.003 0.017 0.197 0.844 -0.031 0.038

CRED -0.005 0.016 -0.311 0.757 -0.037 0.027

FX SDTV3 0.109 0.210 0.517 0.606 -0.307 0.524

FX SDTV5 0.084 0.183 0.460 0.646 -0.278 0.446

Obs. Year -0.007 0.004 -1.699 0.092 -0.015 0.001

Honduras -0.035 0.084 -0.420 0.675 -0.201 0.130

El Salvador -0.046 0.054 -0.857 0.393 -0.152 0.060

Nicaragua -0.058 0.102 -0.567 0.572 -0.259 0.144

Ecuador -0.013 0.057 -0.227 0.821 -0.125 0.100

Peru 0.044 0.057 0.768 0.444 -0.069 0.157

Bolivia -0.068 0.055 -1.235 0.219 -0.177 0.041

85

Appendix XII: WOR without time effects and country dummies





R² 0.163


MSE 0.001

RMSE 0.037

MAPE 460.728

DW 1.848

Cp 19.000

AIC -1015.111

SBC -957.163

PC 1.069



Model 18 0.036 0.002 1.482 0.105

Error 137 0.183 0.001



Model parameters:


Intercept 0.222 0.089 2.504 0.013 0.047 0.397

AGE 0.001 0.001 1.795 0.075 0.000 0.003

STATUS 0.006 0.014 0.424 0.672 -0.021 0.032

REG -0.007 0.015 -0.452 0.652 -0.036 0.023

LNGLP -0.003 0.004 -0.872 0.385 -0.011 0.004

GLPGROWTH -0.048 0.013 -3.572 0.000 -0.075 -0.021

OUTREACH -0.027 0.015 -1.819 0.071 -0.056 0.002

GROWTH 0.125 0.208 0.601 0.549 -0.286 0.535

LNGDP -0.018 0.011 -1.632 0.105 -0.040 0.004

INFL -0.036 0.102 -0.347 0.729 -0.238 0.167

GROWTH SDTV5 0.001 0.006 0.217 0.828 -0.010 0.013

GROWTH SDTV3 -0.006 0.006 -1.034 0.303 -0.017 0.005

PV -0.004 0.017 -0.237 0.813 -0.037 0.029

IRC -0.017 0.011 -1.593 0.113 -0.038 0.004

IRC YEAR 0.013 0.016 0.845 0.399 -0.018 0.045

IRC YEAR +1 0.019 0.015 1.251 0.213 -0.011 0.049

CRED 0.010 0.015 0.651 0.516 -0.019 0.038

FX SDTV3 0.146 0.166 0.876 0.383 -0.183 0.475

FX SDTV5 -0.107 0.132 -0.809 0.420 -0.369 0.155

86

Appendix XIII: (PAR-30+WOR)





R² 0.413


MSE 0.003

RMSE 0.055

MAPE 118.490

DW 1.402

Cp 26.000

AIC -986.260

SBC -904.124

PC 0.793



Model 25 0.314 0.013 4.169 < 0.0001

Error 148 0.446 0.003



Model parameters:


Intercept 20.361 10.371 1.963 0.051 -0.133 40.855

AGE 0.001 0.001 0.985 0.326 -0.001 0.003

STATUS -0.012 0.024 -0.515 0.607 -0.060 0.035

REG -0.016 0.024 -0.685 0.495 -0.063 0.031

LNGLP 0.009 0.007 1.232 0.220 -0.005 0.023

GLPGROWTH -0.045 0.020 -2.280 0.024 -0.084 -0.006

OUTREACH -0.028 0.021 -1.307 0.193 -0.070 0.014

GROWTH -1.008 0.406 -2.483 0.014 -1.809 -0.206

LNGDP -0.023 0.061 -0.383 0.702 -0.144 0.097

INFL 0.169 0.159 1.062 0.290 -0.145 0.483

GROWTH SDTV5 -0.021 0.010 -2.035 0.044 -0.042 -0.001

GROWTH SDTV3 0.002 0.008 0.277 0.782 -0.014 0.018

PV -0.050 0.030 -1.676 0.096 -0.110 0.009

IRC 0.028 0.028 0.979 0.329 -0.028 0.084

IRC YEAR -0.016 0.027 -0.579 0.563 -0.070 0.038

IRC YEAR +1 0.005 0.026 0.208 0.836 -0.046 0.057

CRED -0.002 0.024 -0.097 0.923 -0.049 0.045

FX SDTV3 -0.006 0.270 -0.021 0.983 -0.539 0.528

FX SDTV5 0.154 0.247 0.621 0.536 -0.335 0.643

Obs. Year -0.010 0.005 -1.879 0.062 -0.021 0.001

Honduras 0.025 0.118 0.211 0.833 -0.208 0.257

El Salvador 0.013 0.078 0.170 0.865 -0.140 0.167

Nicaragua -0.072 0.140 -0.512 0.609 -0.348 0.205

Ecuador -0.055 0.078 -0.706 0.482 -0.209 0.099

Peru 0.066 0.079 0.835 0.405 -0.090 0.222

Bolivia -0.115 0.077 -1.489 0.139 -0.267 0.038

87

Appendix XIV: (PAR-30+WOR) without time effects and country

dummies





R² 0.191


MSE 0.004

RMSE 0.063

MAPE 179.102

DW 1.169

Cp 19.000

AIC -944.354

SBC -884.332

PC 1.007



Model 18 0.145 0.008 2.032 0.011

Error 155 0.615 0.004



Model parameters:


Intercept 0.521 0.142 3.669 0.000 0.241 0.802

AGE 0.002 0.001 1.468 0.144 -0.001 0.004

STATUS 0.018 0.022 0.838 0.403 -0.025 0.061

REG 0.000 0.024 -0.011 0.991 -0.048 0.048

LNGLP -0.015 0.006 -2.392 0.018 -0.028 -0.003

GLPGROWTH -0.072 0.022 -3.321 0.001 -0.115 -0.029

OUTREACH -0.012 0.024 -0.512 0.609 -0.059 0.035

GROWTH 0.144 0.333 0.431 0.667 -0.514 0.802

LNGDP -0.032 0.018 -1.801 0.074 -0.067 0.802

INFL 0.092 0.159 0.578 0.564 -0.222 0.802

GROWTH SDTV5 0.000 0.010 0.037 0.971 -0.019 0.802

GROWTH SDTV3 -0.005 0.008 -0.645 0.520 -0.021 0.802

PV -0.033 0.026 -1.271 0.206 -0.085 0.802

IRC -0.021 0.017 -1.275 0.204 -0.055 0.802

IRC YEAR 0.004 0.025 0.175 0.861 -0.045 0.802

IRC YEAR +1 0.011 0.025 0.435 0.664 -0.038 0.802

CRED 0.030 0.024 1.264 0.208 -0.017 0.802

FX SDTV3 0.010 0.237 0.044 0.965 -0.458 0.802

FX SDTV5 -0.156 0.202 -0.774 0.440 -0.555 0.802

88

Appendix XV: LLR

Regression of variable LLR:





R² 0.244


MSE 0.001

RMSE 0.033

MAPE 414.069

DW 2.077

Cp 26.000

AIC -1036.567

SBC -957.271

PC 1.058



Model 25 0.047 0.002 1.680 0.033

Error 130 0.145 0.001



Model parameters:

Source Value Standard error t Pr > |t| Lower bound (95%)Upper bound (95%)

Intercept 13.099 7.494 1.748 0.083 -1.728 27.925

AGE 0.001 0.001 1.655 0.100 0.000 0.003

STATUS 0.012 0.015 0.805 0.422 -0.018 0.043

REG -0.018 0.016 -1.146 0.254 -0.049 0.013

LNGLP 0.004 0.005 0.926 0.356 -0.005 0.014

GLPGROWTH -0.030 0.013 -2.343 0.021 -0.056 -0.005

OUTREACH -0.036 0.014 -2.586 0.011 -0.064 -0.008

GROWTH -0.452 0.270 -1.675 0.096 -0.985 0.082

LNGDP 0.003 0.044 0.078 0.938 -0.083 0.090

INFL 0.000 0.115 -0.002 0.999 -0.229 0.228

GROWTH SDTV5 -0.014 0.007 -2.131 0.035 -0.028 -0.001

GROWTH SDTV3 -0.005 0.006 -0.795 0.428 -0.018 0.008

PV 0.028 0.021 1.340 0.183 -0.014 0.070

IRC 0.029 0.021 1.379 0.170 -0.012 0.070

IRC YEAR -0.014 0.019 -0.726 0.469 -0.051 0.024

IRC YEAR +1 0.004 0.017 0.210 0.834 -0.030 0.037

CRED -0.006 0.015 -0.391 0.696 -0.036 0.024

FX SDTV3 0.079 0.202 0.390 0.697 -0.320 0.478

FX SDTV5 0.103 0.176 0.586 0.559 -0.245 0.451

Obs. Year -0.007 0.004 -1.688 0.094 -0.014 0.001

Honduras -0.012 0.080 -0.153 0.878 -0.171 0.147

El Salvador -0.031 0.051 -0.603 0.548 -0.133 0.071

Nicaragua -0.028 0.098 -0.281 0.779 -0.221 0.166

Ecuador 0.001 0.055 0.026 0.979 -0.107 0.110

Peru 0.049 0.055 0.892 0.374 -0.060 0.157

Bolivia -0.048 0.053 -0.898 0.371 -0.153 0.057

89

Appendix XVI: LLR without time effects and country dummies





R² 0.147


MSE 0.001

RMSE 0.035

MAPE 532.632

DW 1.999

Cp 19.000

AIC -1031.789

SBC -973.842

PC 1.089



Model 18 0.028 0.002 1.317 0.187

Error 137 0.164 0.001



Model parameters:


Intercept 0.192 0.084 2.287 0.024 0.026 0.358

AGE 0.001 0.001 1.329 0.186 0.000 0.002

STATUS 0.003 0.013 0.252 0.801 -0.022 0.029

REG -0.004 0.014 -0.253 0.801 -0.032 0.024

LNGLP -0.003 0.004 -0.755 0.452 -0.010 0.005

GLPGROWTH -0.043 0.013 -3.367 0.001 -0.068 -0.018

OUTREACH -0.025 0.014 -1.810 0.073 -0.053 0.002

GROWTH 0.081 0.197 0.412 0.681 -0.308 0.470

LNGDP -0.015 0.011 -1.412 0.160 -0.036 0.006

INFL -0.021 0.097 -0.211 0.833 -0.213 0.172

GROWTH SDTV5 0.000 0.006 -0.011 0.991 -0.011 0.011

GROWTH SDTV3 -0.005 0.005 -0.941 0.348 -0.016 0.006

PV 0.003 0.016 0.179 0.858 -0.028 0.034

IRC -0.013 0.010 -1.312 0.192 -0.034 0.007

IRC YEAR 0.013 0.015 0.839 0.403 -0.017 0.042

IRC YEAR +1 0.018 0.014 1.262 0.209 -0.010 0.046

CRED 0.006 0.014 0.449 0.654 -0.021 0.034

FX SDTV3 0.122 0.158 0.773 0.441 -0.190 0.434

FX SDTV5 -0.069 0.125 -0.552 0.582 -0.317 0.179

90

Appendix XVII: RCR

Regression of variable RCR:





R² 0.352


MSE 11.412

RMSE 3.378

MAPE 150.230

DW 1.783

Cp 26.000

AIC 447.474

SBC 529.610

PC 0.875



Model 25 918.578 36.743 3.220 < 0.0001

Error 148 1689.006 11.412



Model parameters:

Source Value Standard error t Pr > |t| Lower bound (95%)Upper bound (95%)

Intercept -154.678 638.368 -0.242 0.809 -1416.170 1106.815

AGE 0.152 0.069 2.215 0.028 0.016 0.287

STATUS 1.295 1.482 0.874 0.384 -1.633 4.223

REG -0.371 1.464 -0.253 0.800 -3.264 2.522

LNGLP -0.191 0.434 -0.441 0.660 -1.048 0.665

GLPGROWTH 3.100 1.221 2.539 0.012 0.687 5.513

OUTREACH -3.272 1.313 -2.491 0.014 -5.867 -0.676

GROWTH -33.304 24.974 -1.334 0.184 -82.657 16.048

LNGDP 2.555 3.767 0.678 0.499 -4.889 9.999

INFL 8.300 9.789 0.848 0.398 -11.045 27.645

GROWTH SDTV5 1.277 0.642 1.987 0.049 0.007 2.546

GROWTH SDTV3 0.024 0.497 0.048 0.962 -0.959 1.007

PV 1.581 1.851 0.854 0.394 -2.076 5.239

IRC 0.622 1.753 0.355 0.723 -2.842 4.086

IRC YEAR -0.804 1.683 -0.478 0.633 -4.130 2.521

IRC YEAR +1 -0.960 1.596 -0.602 0.548 -4.113 2.193

CRED -1.325 1.458 -0.909 0.365 -4.207 1.557

FX SDTV3 -38.876 16.623 -2.339 0.021 -71.725 -6.027

FX SDTV5 36.389 15.234 2.389 0.018 6.285 66.493

Obs. Year 0.066 0.330 0.201 0.841 -0.585 0.718

Honduras 4.449 7.244 0.614 0.540 -9.867 18.764

El Salvador 2.862 4.779 0.599 0.550 -6.581 12.306

Nicaragua 7.161 8.619 0.831 0.407 -9.872 24.195

Ecuador 2.378 4.804 0.495 0.621 -7.117 11.872

Peru 4.075 4.853 0.840 0.402 -5.514 13.665

Bolivia 8.915 4.740 1.881 0.062 -0.451 18.281

91

Appendix XVIII: RCR without time effects and country dummies

Regression of variable RCR:





R² 0.248


MSE 12.650

RMSE 3.557

MAPE 181.654

DW 1.666

Cp 19.000

AIC 459.437

SBC 519.459

PC 0.936



Model 18 646.780 35.932 2.840 0.000

Error 155 1960.803 12.650



Model parameters:


Intercept -3.641 8.028 -0.454 0.651 -19.500 12.218

AGE 0.140 0.070 1.997 0.048 0.001 0.278

STATUS -0.589 1.224 -0.481 0.631 -3.007 1.829

REG -0.356 1.372 -0.259 0.796 -3.065 2.354

LNGLP 0.330 0.360 0.916 0.361 -0.382 1.042

GLPGROWTH 3.386 1.229 2.755 0.007 0.958 5.814

OUTREACH -3.496 1.341 -2.607 0.010 -6.145 -0.847

GROWTH -57.416 18.813 -3.052 0.003 -94.579 -20.254

LNGDP 0.357 1.001 0.357 0.722 -1.620 2.334

INFL -2.595 8.992 -0.289 0.773 -20.358 15.169

GROWTH SDTV5 0.548 0.549 0.999 0.319 -0.536 1.632

GROWTH SDTV3 -0.521 0.451 -1.157 0.249 -1.411 0.369

PV -0.015 1.482 -0.010 0.992 -2.942 2.912

IRC 0.496 0.949 0.523 0.602 -1.379 2.372

IRC YEAR -1.455 1.414 -1.029 0.305 -4.247 1.338

IRC YEAR +1 -1.447 1.410 -1.026 0.307 -4.232 1.339

CRED -3.067 1.334 -2.298 0.023 -5.703 -0.431

FX SDTV3 -5.164 13.381 -0.386 0.700 -31.596 21.269

FX SDTV5 7.722 11.396 0.678 0.499 -14.790 30.233